Updated on 2024/04/01

写真a

 
MORI, Kensaku
 
Organization
Graduate School of Informatics Department of Intelligent Systems 2 Professor
Graduate School
Graduate School of Information Science
Graduate School of Informatics
Undergraduate School
School of Informatics Department of Computer Science
Title
Professor
Contact information
メールアドレス
External link

Degree 1

  1. 博士(工学)

Research Interests 2

  1. Image processing, high-dimensional image processing, pattern recognition, machine learning, medical image processing, computer assisted surgery, computer aided diagnosis, artificial intelligence

  2. artificial intelligence

Research Areas 3

  1. Others / Others  / Visual Information Processing

  2. Others / Others  / Medical Systems

  3. Others / Others  / Intelligent Informatics

Current Research Project and SDGs 1

  1. Study on 3-D image processing and its medical applications

Research History 11

  1. Nagoya University   Administrative Support Organizations

    2017.4

  2. Nagoya University   Information Technology Center   Director in General

    2016.4

  3. Nagoya University   Administrative Support Organizations Information Strategy Office   Head

    2016.4

  4. Nagoya University   Graduate School of Information Science Department of Media Science Intelligent Media Engineering   Professor

    2009.10 - 2017.3

  5. Associate Professor, Dept. of Media Science, Graduate School of Information Science, Nagoya University

    2007.4

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    Country:Japan

  6. Associate Professor, Dept. of Media Science, Graduate School of Information Science, Nagoya University

    2003.4 - 2007.3

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    Country:Japan

  7. 名古屋大学難処理人工物研究センター助教授

    2001.4 - 2003.3

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    Country:Japan

  8. 名古屋大学大学院工学研究科計算理工学専攻講師

    2000.4 - 2001.3

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    Country:Japan

  9. 名古屋大学大学院工学研究科計算理工学専攻助手

    1997.4 - 2000.3

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    Country:Japan

  10. 日本学術振興会特別研究員(PD)

    1996.10

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    Country:Japan

  11. 日本学術振興会特別研究員(DC1)

    1994.4

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    Country:Japan

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Education 3

  1. Nagoya University   Graduate School, Division of Engineering

    1994.4 - 1996.9

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    Country: Japan

  2. Nagoya University   Graduate School, Division of Engineering

    1992.4 - 1994.3

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    Country: Japan

  3. Nagoya University   Faculty of Engineering

    1988.4 - 1992.3

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    Country: Japan

Professional Memberships 38

  1. 日本医用画像工学会

    2020

  2. 電子情報通信学会   医用画像研究会専門委員会幹事

    2007.4

  3. 日本医用画像工学会   幹事

    2009.4

  4. 日本生体医工学会   代議員

    2007.4 - 2010.3

  5. IEEE

  6. SPIE   SPIE Medical Imaging Program Committee

    2005.4

  7. 情報処理学会

    2020.4

  8. 日本がん検診・診断学会

    2020.4

  9. 日本呼吸内視鏡学会

    2020.4

  10. 日本生体医工学会

    2020.4

  11. 東海支部連合大会

    2020.4

  12. 日本CT検診学会

    2020.4

  13. 日本消化器内視鏡学会

    2020.4

  14. 日本VR医学会   評議員

    2009.4 - 2011.3

  15. 日本コンピュータ外科学会   理事

    2008.4

  16. 電子情報通信学会   医用画像研究会幹事

    2007.4

  17. MICCAI (Medical Image Computing and Computer Assisted Surgery   Program Committee

    2007.4

  18. SPIE Medical Imaging Conference 2007, Computer Aided Diagnosis   Program Committee

    2007.4 - 2009.3

  19. SPIE Medical Imaging Conference 2007, Image Processing   Program Committee

    2007.4 - 2009.3

  20. 電子情報通信学会   医療情報通信技術時限研究専門委員会専門委員

    2006.4 - 2008.3

  21. SPIE Medical Imaging Conference 2007, Image Processing   Program Committee

    2006.4 - 2008.3

  22. SPIE Medical Imaging Conference 2007, Computer Aided Diagnosis   Program Committee

    2006.4 - 2008.3

  23. コンピュータ支援画像診断学会   評議員

    2005.4

  24. International Journal of Computer Assisted Radiology and Surgery   Editorial Board

    2005.4

  25. Journal Medical Image Analysis   Editorial Board

    2005.4

  26. 電子情報通信学会   医用画像研究会幹事補佐

    2005.4 - 2008.3

  27. MICCAI (Medical Image Computing and Computer Assisted Intervention)   Area chair

    2005.4 - 2007.3

  28. SPIE Medical Imaging Conference 2006, Image Processing   Program Committee

    2005.4 - 2007.3

  29. MICCAI (Medical Image Computing and Computer Assisted Intervention)   Reviewer

    2004.4 - 2006.3

  30. 電子情報通信学会   医用画像研究会専門委員

    2003.4 - 2005.3

  31. 日本エム・イー学会   研究奨励賞選定委員会委員

    2003.4 - 2005.3

  32. MICCAI (Medical Image Computing and Computer Assisted Intervention)   Referee

    2003.4 - 2004.3

  33. Japan Society of Computer Assisted Syrgery

    2002.4

  34. CARS (Computer Assisted Radiology and Surgery)   Program Committee

    2001.4 - 2008.3

  35. Medical Image Analysis   Ad hoc Reviewer

  36. IEEE Transactions on Medical Imaging   Ad hoc Reviewer

  37. CARS (Computer Assisted Radiology and Surgery)

  38. International Journal of Computer Assisted Radiology and Surgery

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Committee Memberships 27

  1. 学内   TMI卓越大学院広報委員会委員  

    2020.12   

  2. 一般財団法人高度情報科学技術研究機構   HPCI連携サービス委員  

    2020.5 - 2021.3   

  3. 学内   東山キャンパス倫理審査委員  

    2020.4   

  4. 学内   情報連携推進本部業務会議 委員  

    2020.4   

  5. 学内   情報メディア教育システム運営協議会  

    2020.4   

  6. 学内   情報セキュリティ組織連絡協議会委員  

    2020.4   

  7. 学内   情報メディア教育システム専門委員  

    2020.4   

  8. 学内   プロジェクト・業務専門員会委員  

    2020.4   

  9. 学内   セキュリティ専門委員  

    2020.4   

  10. 学内   全国共同利用システム専門委員  

    2020.4   

  11. 学内   情報連携推進本部会議  

    2020.4   

  12. 学内   情報連携統括本部情報戦略室  

    2020.4   

  13. 学内   情報連携統括本部会議委員  

    2020.4   

  14. 学内   数理・データ科学教育研究センター 教育専門委員  

    2020.4   

  15. 学内   数理・データ科学教育研究センター運営委員  

    2020.4   

  16. 学内   全学技術センター運営専門委員会技術支援室委員  

    2020.4   

  17. 学内   全学技術センター運営委員会運営専門委員  

    2020.4   

  18. 学内   全学技術センター運営委員会人事委員  

    2020.4   

  19. 学内   連合第2群会議委員  

    2020.4   

  20. 学内   将来構想分科会委員  

    2020.4   

  21. 学内   教育分科会(全学教育委員会)委員  

    2020.4   

  22. 学内   安全保障委員  

    2020.4   

  23. 公益財団法人 テルモ生命科学振興財団   研究開発助成選考委員  

    2020.4 - 2022.3   

  24. 国立研究開発法人医薬基盤・健康・栄養研究所   国立研究開発法人医薬基盤・健康・栄養研究所SIP評価委員  

    2020.4 - 2022.3   

  25. 学内   CIBoG 運営委員  

    2019.4   

  26. 学内   CIBoGカリキュラム委員  

    2019.4   

  27. 一般社団法人 日本コンピュータ外科学会   国際委員会委員  

    2017.12 - 2019.9   

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Awards 21

  1. The Commendation for Science and Technology by the Minister of Education, Culture, Sports, Science and Technology

    2022.4   MEXT  

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    Award type:International academic award (Japan or overseas)  Country:Japan

  2. 日本医用画像工学会論文賞

    2009.8   日本医用画像工学会  

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    Country:Japan

    計算機支援医用画像のための共通基盤システムの開発

  3. 功績賞

    2021.10   日本医用画像工学会  

  4. 文部科学大臣表彰 若手科学者賞

    2006.4   文部科学省  

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    Country:Japan

    仮想化内視鏡システム開発に対する貢献が評価されたものである。

  5. Certificate of Merit, Education Exhibit, Radiological Society of North America

    2009.11   RSNA (Radiological Society of North America)  

  6. 日本コンピュータ外科学会 2016年度講演論文賞

    2017.10   一般社団法人 日本コンピュータ外科学会  

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    Award type:Award from Japanese society, conference, symposium, etc.  Country:Japan

  7. Fellow

    2015.10   MICCAI  

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    Country:Germany

  8. Magna Cum Laude Education Exhibit

    2014.12   Radiological Society of North America  

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    Country:United States

  9. Conference 8315, Honorable Mention Poster Award

    2012.2   SPIE Medical Imaging 2012: Computer-Aided Diagnosis  

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    Country:United States

  10. Honorable Mention Poster Award

    2011.2   SPIE Medical Imaging 2011: Image Processing  

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    Country:United States

  11. 2009年度 CAS Young Investigator Award ゴールド賞(日立メディコ賞)

    2010.11   第19回日本コンピュータ外科学会大会  

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    Country:Japan

  12. Honorable Mention Poster Award

    2010.2   SPIE Medical Imaging 2010: Computer-Aided Diagnosi  

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    Country:United States

  13. Certificate of Merit Education Exhibit

    2009.11   RSNA(Radiological Society of North America)  

  14. Certificate of Merit

    2004.11   Radiological Society of North America  

  15. International Society of Computer Assisted Surgery

    2004.6   Best Poster Award (1st Prize)  

  16. 電子情報通信学会ソサイエティ論文賞

    2000  

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    Country:Japan

  17. 日本気管支学会論文賞

    2000  

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    Country:Japan

  18. 丹羽記念賞

    1998  

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    Country:Japan

  19. 日本医用画像工学会奨励賞

    1997  

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    Country:Japan

  20. 日本エム・イー学会論文賞

    1997  

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    Country:Japan

  21. 日本医用画像工学会奨励賞

    1995  

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    Country:Japan

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Papers 1772

  1. Label cleaning and propagation for improved segmentation performance using fully convolutional networks Reviewed

    Takaaki Sugino, Yutaro Suzuki, Taichi Kin, Nobuhito Saito, Shinya Onogi, Toshihiro Kawase, Kensaku Mori, Yoshikazu Nakajima

    International Journal of Computer Assisted Radiology and Surgery     2021.3

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    Language:English   Publishing type:Research paper (scientific journal)  

    DOI: https://doi.org/10.1007/s11548-021-02312-5

  2. Predicting Violence Rating Based on Pairwise Comparison. Reviewed

    Ying JI, Yu WANG, Jien KATO, Kensaku MORI

    IEICE Transactions on Information and Systems   Vol. E103.D ( 12 ) page: 2578 - 2589   2020.12

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    Authorship:Last author   Language:English   Publishing type:Research paper (scientific journal)  

    DOI: https://doi.org/10.1587/transinf.2020EDP7056

  3. Anatomical attention can help to segment the dilated pancreatic duct in abdominal CT. Invited Reviewed

    Shen C, Roth HR, Hayashi Y, Oda M, Sato G, Miyamoto T, Rueckert D, Mori K

    International journal of computer assisted radiology and surgery   Vol. 19 ( 4 ) page: 655 - 664   2024.4

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    Authorship:Corresponding author   Language:English   Publishing type:Research paper (scientific journal)   Publisher:International Journal of Computer Assisted Radiology and Surgery  

    Purpose: Pancreatic duct dilation is associated with an increased risk of pancreatic cancer, the most lethal malignancy with the lowest 5-year relative survival rate. Automatic segmentation of the dilated pancreatic duct from contrast-enhanced CT scans would facilitate early diagnosis. However, pancreatic duct segmentation poses challenges due to its small anatomical structure and poor contrast in abdominal CT. In this work, we investigate an anatomical attention strategy to address this issue. Methods: Our proposed anatomical attention strategy consists of two steps: pancreas localization and pancreatic duct segmentation. The coarse pancreatic mask segmentation is used to guide the fully convolutional networks (FCNs) to concentrate on the pancreas’ anatomy and disregard unnecessary features. We further apply a multi-scale aggregation scheme to leverage the information from different scales. Moreover, we integrate the tubular structure enhancement as an additional input channel of FCN. Results: We performed extensive experiments on 30 cases of contrast-enhanced abdominal CT volumes. To evaluate the pancreatic duct segmentation performance, we employed four measurements, including the Dice similarity coefficient (DSC), sensitivity, normalized surface distance, and 95 percentile Hausdorff distance. The average DSC achieves 55.7%, surpassing other pancreatic duct segmentation methods on single-phase CT scans only. Conclusions: We proposed an anatomical attention-based strategy for the dilated pancreatic duct segmentation. Our proposed strategy significantly outperforms earlier approaches. The attention mechanism helps to focus on the pancreas region, while the enhancement of the tubular structure enables FCNs to capture the vessel-like structure. The proposed technique might be applied to other tube-like structure segmentation tasks within targeted anatomies.

    DOI: 10.1007/s11548-023-03049-z

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  4. Artificial intelligence-based diagnostic imaging system with virtual enteroscopy and virtual unfolded views to evaluate small bowel lesions in Crohn's disease. Invited Reviewed

    Furukawa K, Oda M, Watanabe O, Nakamura M, Yamamura T, Maeda K, Mori K, Kawashima H

    Revista espanola de enfermedades digestivas     2024.3

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    Authorship:Corresponding author   Language:English   Publishing type:Research paper (scientific journal)  

    DOI: 10.17235/reed.2024.10405/2024

    PubMed

  5. Development of real-time navigation system for laparoscopic hepatectomy using magnetic micro sensor. Invited Reviewed

    Igami T, Hayashi Y, Yokyama Y, Mori K, Ebata T

    Minimally invasive therapy & allied technologies : MITAT : official journal of the Society for Minimally Invasive Therapy     page: 1 - 11   2024.1

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    Authorship:Corresponding author   Language:English   Publishing type:Research paper (scientific journal)   Publisher:Minimally Invasive Therapy and Allied Technologies  

    Background: We report a new real-time navigation system for laparoscopic hepatectomy (LH), which resembles a car navigation system. Material and methods: Virtual three-dimensional liver and body images were reconstructed using the “New-VES” system, which worked as roadmap during surgery. Several points of the patient’s body were registered in virtual images using a magnetic position sensor (MPS). A magnetic transmitter, corresponding to an artificial satellite, was placed about 40 cm above the patient’s body. Another MPS, corresponding to a GPS antenna, was fixed on the handling part of the laparoscope. Fiducial registration error (FRE, an error between real and virtual lengths) was utilized to evaluate the accuracy of this system. Results: Twenty-one patients underwent LH with this system. Mean FRE of the initial five patients was 17.7 mm. Mean FRE of eight patients in whom MDCT was taken using radiological markers for registration of body parts as first improvement, was reduced to 10.2 mm (p =.014). As second improvement, a new MPS as an intraoperative body position sensor was fixed on the right-sided chest wall for automatic correction of postural gap. The preoperative and postoperative mean FREs of 8 patients with both improvements were 11.1 mm and 10.1 mm (p =.250). Conclusions: Our system may provide a promising option that virtually guides LH.

    DOI: 10.1080/13645706.2023.2301594

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  6. Deep learning model for extensive smartphone-based diagnosis and triage of cataracts and multiple corneal diseases. Invited Reviewed

    Ueno Y, Oda M, Yamaguchi T, Fukuoka H, Nejima R, Kitaguchi Y, Miyake M, Akiyama M, Miyata K, Kashiwagi K, Maeda N, Shimazaki J, Noma H, Mori K, Oshika T

    The British journal of ophthalmology     2024.1

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    Authorship:Corresponding author   Language:English   Publishing type:Research paper (scientific journal)   Publisher:British Journal of Ophthalmology  

    Aim To develop an artificial intelligence (AI) algorithm that diagnoses cataracts/corneal diseases from multiple conditions using smartphone images. Methods This study included 6442 images that were captured using a slit-lamp microscope (6106 images) and smartphone (336 images). An AI algorithm was developed based on slit-lamp images to differentiate 36 major diseases (cataracts and corneal diseases) into 9 categories. To validate the AI model, smartphone images were used for the testing dataset. We evaluated AI performance that included sensitivity, specificity and receiver operating characteristic (ROC) curve for the diagnosis and triage of the diseases. Results The AI algorithm achieved an area under the ROC curve of 0.998 (95% CI, 0.992 to 0.999) for normal eyes, 0.986 (95% CI, 0.978 to 0.997) for infectious keratitis, 0.960 (95% CI, 0.925 to 0.994) for immunological keratitis, 0.987 (95% CI, 0.978 to 0.996) for cornea scars, 0.997 (95% CI, 0.992 to 1.000) for ocular surface tumours, 0.993 (95% CI, 0.984 to 1.000) for corneal deposits, 1.000 (95% CI, 1.000 to 1.000) for acute angle-closure glaucoma, 0.992 (95% CI, 0.985 to 0.999) for cataracts and 0.993 (95% CI, 0.985 to 1.000) for bullous keratopathy. The triage of referral suggestion using the smartphone images exhibited high performance, in which the sensitivity and specificity were 1.00 (95% CI, 0.478 to 1.00) and 1.00 (95% CI, 0.976 to 1.000) for’urgent’, 0.867 (95% CI, 0.683 to 0.962) and 1.00 (95% CI, 0.971 to 1.000) for’semi-urgent’, 0.853 (95% CI, 0.689 to 0.950) and 0.983 (95% CI, 0.942 to 0.998) for’routine’ and 1.00 (95% CI, 0.958 to 1.00) and 0.896 (95% CI, 0.797 to 0.957) for’observation’, respectively. Conclusions The AI system achieved promising performance in the diagnosis of cataracts and corneal diseases.

    DOI: 10.1136/bjo-2023-324488

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  7. Endoscope Automation Framework with Hierarchical Control and Interactive Perception for Multi-Tool Tracking in Minimally Invasive Surgery. Invited Reviewed

    Fozilov K, Colan J, Davila A, Misawa K, Qiu J, Hayashi Y, Mori K, Hasegawa Y

    Sensors (Basel, Switzerland)   Vol. 23 ( 24 )   2023.12

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    Authorship:Corresponding author   Language:English   Publishing type:Research paper (scientific journal)   Publisher:Sensors  

    In the context of Minimally Invasive Surgery, surgeons mainly rely on visual feedback during medical operations. In common procedures such as tissue resection, the automation of endoscopic control is crucial yet challenging, particularly due to the interactive dynamics of multi-agent operations and the necessity for real-time adaptation. This paper introduces a novel framework that unites a Hierarchical Quadratic Programming controller with an advanced interactive perception module. This integration addresses the need for adaptive visual field control and robust tool tracking in the operating scene, ensuring that surgeons and assistants have optimal viewpoint throughout the surgical task. The proposed framework handles multiple objectives within predefined thresholds, ensuring efficient tracking even amidst changes in operating backgrounds, varying lighting conditions, and partial occlusions. Empirical validations in scenarios involving single, double, and quadruple tool tracking during tissue resection tasks have underscored the system’s robustness and adaptability. The positive feedback from user studies, coupled with the low cognitive and physical strain reported by surgeons and assistants, highlight the system’s potential for real-world application.

    DOI: 10.3390/s23249865

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  8. Artificial intelligence for evaluating the risk of gastric cancer: reliable detection and scoring of intestinal metaplasia with deep learning algorithms. Invited Reviewed

    Iwaya M, Hayashi Y, Sakai Y, Yoshizawa A, Iwaya Y, Uehara T, Kitagawa M, Fukayama M, Mori K, Ota H

    Gastrointestinal endoscopy   Vol. 98 ( 6 ) page: 925 - 933.e1   2023.12

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    Authorship:Corresponding author   Language:English   Publishing type:Research paper (scientific journal)   Publisher:Gastrointestinal Endoscopy  

    Background and Aims: Gastric cancer (GC) is associated with chronic gastritis. To evaluate the risk, the Operative Link on Gastric Intestinal Metaplasia Assessment (OLGIM) system was constructed and showed a higher GC risk in stage III or IV patients, determined by the degree of intestinal metaplasia (IM). Although the OLGIM system is useful, evaluating the degree of IM requires substantial experience to produce precise scoring. Whole-slide imaging is becoming routine, but most artificial intelligence (AI) systems in pathology are focused on neoplastic lesions. Methods: Hematoxylin and eosin–stained slides were scanned. Images were divided into each gastric biopsy tissue sample and labeled with an IM score. IM was scored as follows: 0 (no IM), 1 (mild IM), 2 (moderate IM), and 3 (severe IM). Overall, 5753 images were prepared. A deep convolutional neural network (DCNN) model, ResNet50, was used for classification. Results: ResNet50 classified images with and without IM with a sensitivity of 97.7% and specificity of 94.6%. IM scores 2 and 3, involved as criteria of stage III or IV in the OLGIM system, were classified by ResNet50 in 18%. The respective sensitivity and specificity values of classifying IM between scores 0 and 1 and 2 and 3 were 98.5% and 94.9%, respectively. The IM scores classified by pathologists and the AI system were different in only 438 images (7.6%), and we found that ResNet50 tended to miss small foci of IM but successfully identified minimal IM areas that pathologists missed during the review. Conclusions: Our findings suggested that this AI system would contribute to evaluating the risk of GC accuracy, reliability, and repeatability with worldwide standardization.

    DOI: 10.1016/j.gie.2023.06.056

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  9. Deep learning-based prediction model for postoperative complications of cervical posterior longitudinal ligament ossification Invited Reviewed

    Sadayuki Ito, Hiroaki Nakashima, Toshitaka Yoshii, Satoru Egawa, Kenichiro Sakai, Kazuo Kusano, Shinji Tsutui, Takashi Hirai, Yu Matsukura, Kanichiro Wada, Keiichi Katsumi, Masao Koda, Atsushi Kimura, Takeo Furuya, Satoshi Maki, Narihito Nagoshi, Norihiro Nishida, Yukitaka Nagamoto, Yasushi Oshima, Kei Ando, Masahiko Takahata, Kanji Mori, Hideaki Nakajima, Kazuma Murata, Masayuki Miyagi, Takashi Kaito, Kei Yamada, Tomohiro Banno, Satoshi Kato, Tetsuro Ohba, Satoshi Inami, Shunsuke Fujibayashi, Hiroyuki Katoh, Haruo Kanno, Masahiro Oda, Kensaku Mori, Hiroshi Taneichi, Yoshiharu Kawaguchi, Katsushi Takeshita, Morio Matsumoto, Masashi Yamazaki, Atsushi Okawa, Shiro Imagama

    Europian Spine Journal   Vol. 32 ( 11 ) page: 3797 - 3806   2023.11

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    Authorship:Corresponding author   Language:English   Publishing type:Research paper (scientific journal)  

    DOI: 10.1007/s00586-023-07562-2

    DOI: 10.1007/s00586-023-07562-2

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  10. Social Relation Atmosphere Recognition with Relevant Visual Concepts Invited

    JI Ying, WANG Yu, MORI Kensaku, KATO Jien

    IEICE Transactions on Information and Systems   Vol. E106.D ( 10 ) page: 1638 - 1649   2023.10

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    Authorship:Corresponding author   Language:English   Publishing type:Research paper (scientific journal)   Publisher:The Institute of Electronics, Information and Communication Engineers  

    <p>Social relationships (e.g., couples, opponents) are the foundational part of society. Social relation atmosphere describes the overall interaction environment between social relationships. Discovering social relation atmosphere can help machines better comprehend human behaviors and improve the performance of social intelligent applications. Most existing research mainly focuses on investigating social relationships, while ignoring the social relation atmosphere. Due to the complexity of the expressions in video data and the uncertainty of the social relation atmosphere, it is even difficult to define and evaluate. In this paper, we innovatively analyze the social relation atmosphere in video data. We introduce a Relevant Visual Concept (RVC) from the social relationship recognition task to facilitate social relation atmosphere recognition, because social relationships contain useful information about human interactions and surrounding environments, which are crucial clues for social relation atmosphere recognition. Our approach consists of two main steps: (1) we first generate a group of visual concepts that preserve the inherent social relationship information by utilizing a 3D explanation module; (2) the extracted relevant visual concepts are used to supplement the social relation atmosphere recognition. In addition, we present a new dataset based on the existing Video Social Relation Dataset. Each video is annotated with four kinds of social relation atmosphere attributes and one social relationship. We evaluate the proposed method on our dataset. Experiments with various 3D ConvNets and fusion methods demonstrate that the proposed method can effectively improve recognition accuracy compared to end-to-end ConvNets. The visualization results also indicate that essential information in social relationships can be discovered and used to enhance social relation atmosphere recognition.</p>

    DOI: 10.1587/transinf.2023PCP0008

    Scopus

    CiNii Research

  11. Automated Detection and Diagnosis of Spinal Schwannomas and Meningiomas Using Deep Learning and Magnetic Resonance Imaging. Invited Reviewed

    Ito S, Nakashima H, Segi N, Ouchida J, Oda M, Yamauchi I, Oishi R, Miyairi Y, Mori K, Imagama S

    Journal of clinical medicine   Vol. 12 ( 15 )   2023.8

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    Authorship:Corresponding author   Language:English   Publishing type:Research paper (scientific journal)   Publisher:Journal of Clinical Medicine  

    Spinal cord tumors are infrequently identified spinal diseases that are often difficult to diagnose even with magnetic resonance imaging (MRI) findings. To minimize the probability of overlooking these tumors and improve diagnostic accuracy, an automatic diagnostic system is needed. We aimed to develop an automated system for detecting and diagnosing spinal schwannomas and meningiomas based on deep learning using You Only Look Once (YOLO) version 4 and MRI. In this retrospective diagnostic accuracy study, the data of 50 patients with spinal schwannomas, 45 patients with meningiomas, and 100 control cases were reviewed, respectively. Sagittal T1-weighted (T1W) and T2-weighted (T2W) images were used for object detection, classification, training, and validation. The object detection and diagnosis system was developed using YOLO version 4. The accuracies of the proposed object detections based on T1W, T2W, and T1W + T2W images were 84.8%, 90.3%, and 93.8%, respectively. The accuracies of the object detection for two spine surgeons were 88.9% and 90.1%, respectively. The accuracies of the proposed diagnoses based on T1W, T2W, and T1W + T2W images were 76.4%, 83.3%, and 84.1%, respectively. The accuracies of the diagnosis for two spine surgeons were 77.4% and 76.1%, respectively. We demonstrated an accurate, automated detection and diagnosis of spinal schwannomas and meningiomas using the developed deep learning-based method based on MRI. This system could be valuable in supporting radiological diagnosis of spinal schwannomas and meningioma, with a potential of reducing the radiologist’s overall workload.

    DOI: 10.3390/jcm12155075

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  12. Activities of National Institute of Informatics in Japan Invited Reviewed

    Kitsuregawa M., Urushidani S., Yamaji K., Takakura H., Hasuo I., Sato I., Ishikawa F., Echizen I., Mori K.

    Communications of the ACM   Vol. 66 ( 7 ) page: 58 - 63   2023.6

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    DOI: 10.1145/3589736

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  13. Speedometer for withdrawal time monitoring during colonoscopy: a clinical implementation trial. Invited Reviewed

    Barua I, Misawa M, Glissen Brown JR, Walradt T, Kudo SE, Sheth SG, Nee J, Iturrino J, Mukherjee R, Cheney CP, Sawhney MS, Pleskow DK, Mori K, Løberg M, Kalager M, Wieszczy P, Bretthauer M, Berzin TM, Mori Y

    Scandinavian journal of gastroenterology   Vol. 58 ( 6 ) page: 664 - 670   2023.6

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    Objectives: Meticulous inspection of the mucosa during colonoscopy, represents a lengthier withdrawal time, but has been shown to increase adenoma detection rate (ADR). We investigated if artificial intelligence-aided speed monitoring can improve suboptimal withdrawal time. Methods: We evaluated the implementation of a computer-aided speed monitoring device during colonoscopy at a large academic endoscopy center. After informed consent, patients ≥18 years undergoing colonoscopy between 5 March and 29 April 2021 were examined without the use of the speedometer, and with the speedometer between 29 April and 30 June 2021. All colonoscopies were recorded, and withdrawal time was assessed based on the recordings in a blinded fashion. We compared mean withdrawal time, percentage of withdrawal time ≥6 min, and ADR with and without the speedometer. Results: One hundred sixty-six patients in each group were eligible for analyses. Mean withdrawal time was 9 min and 6.6 s (95% CI: 8 min and 34.8 s to 9 min and 39 s) without the use of the speedometer, and 9 min and 9 s (95% CI: 8 min and 45 s to 9 min and 33.6 s) with the speedometer; difference 2.3 s (95% CI: −42.3–37.7, p = 0.91). The ADRs were 45.2% (95% CI: 37.6–52.8) without the speedometer as compared to 45.8% (95% CI: 38.2–53.4) with the speedometer (p = 0.91). The proportion of colonoscopies with withdrawal time ≥6 min without the speedometer was 85.5% (95% CI: 80.2–90.9) versus 86.7% (95% CI: 81.6–91.9) with the speedometer (p = 0.75). Conclusions: Use of speed monitoring during withdrawal did not increase withdrawal time or ADR in colonoscopy. ClinicalTrials.gov Identifier: NCT04710251.

    DOI: 10.1080/00365521.2022.2154616

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  14. Correction to: Gaussian affinity and GIoU-based loss for perforation detection and localization from colonoscopy videos. Invited Reviewed

    Jiang K, Itoh H, Oda M, Okumura T, Mori Y, Misawa M, Hayashi T, Kudo SE, Mori K

    International journal of computer assisted radiology and surgery   Vol. 18 ( 5 ) page: 807   2023.5

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    The original version of this article unfortunately contained a mistake. The incorrect notations were given in the author’s affiliations.

    DOI: 10.1007/s11548-023-02899-x

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  15. Gaussian affinity and GIoU-based loss for perforation detection and localization from colonoscopy videos. Invited Reviewed

    Jiang K, Itoh H, Oda M, Okumura T, Mori Y, Misawa M, Hayashi T, Kudo SE, Mori K

    International journal of computer assisted radiology and surgery   Vol. 18 ( 5 ) page: 795 - 805   2023.5

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    Purpose: Endoscopic submucosal dissection (ESD) is a minimally invasive treatment for early gastric cancer. However, perforations may happen and cause peritonitis during ESD. Thus, there is a potential demand for a computer-aided diagnosis system to support physicians in ESD. This paper presents a method to detect and localize perforations from colonoscopy videos to avoid perforation ignoring or enlarging by ESD physicians. Method: We proposed a training method for YOLOv3 by using GIoU and Gaussian affinity losses for perforation detection and localization in colonoscopic images. In this method, the object functional contains the generalized intersection over Union loss and Gaussian affinity loss. We propose a training method for the architecture of YOLOv3 with the presented loss functional to detect and localize perforations precisely. Results: To qualitatively and quantitatively evaluate the presented method, we created a dataset from 49 ESD videos. The results of the presented method on our dataset revealed a state-of-the-art performance of perforation detection and localization, which achieved 0.881 accuracy, 0.869 AUC, and 0.879 mean average precision. Furthermore, the presented method is able to detect a newly appeared perforation in 0.1 s. Conclusions: The experimental results demonstrated that YOLOv3 trained by the presented loss functional were very effective in perforation detection and localization. The presented method can quickly and precisely remind physicians of perforation happening in ESD. We believe a future CAD system can be constructed for clinical applications with the proposed method.

    DOI: 10.1007/s11548-022-02821-x

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  16. Class-wise confidence-aware active learning for laparoscopic images segmentation Invited Reviewed

    Jie Qiu, Yuichiro Hayashi, Masahiro Oda, Takayuki Kitasaka, Kensaku Mori

    International Journal of Computer Assisted Radiology and Surgery   Vol. 18 ( 3 ) page: 473 - 482   2023.3

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    DOI: 10.1007/s11548-022-02773-2

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  17. A skeleton context-aware 3D fully convolutional network for abdominal artery segmentation Invited Reviewed

    Ruiyun Zhu, Masahiro Oda, Yuichiro Hayashi, Takayuki Kitasaka, Kazunari Misawa, Michitaka Fujiwara & Kensaku Mori   Vol. 18 ( 3 ) page: 461 - 472   2023.3

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    DOI: 10.1007/s11548-022-02767-0

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  18. Deep Learning-Based Seminal Vesicle and Vas Deferens Recognition in the Posterior Approach of Robot-Assisted Radical Prostatectomy. Invited Reviewed

    Takeshita N, Sakamoto S, Kitaguchi D, Takeshita N, Yajima S, Koike T, Ishikawa Y, Matsuzaki H, Mori K, Masuda H, Ichikawa T, Ito M

    Urology   Vol. 173   page: 98 - 103   2023.3

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    Objective: To develop a convolutional neural network to recognize the seminal vesicle and vas deferens (SV-VD) in the posterior approach of robot-assisted radical prostatectomy (RARP) and assess the performance of the convolutional neural network model under clinically relevant conditions. Methods: Intraoperative videos of robot-assisted radical prostatectomy performed by the posterior approach from 3 institutions were obtained between 2019 and 2020. Using SV-VD dissection videos, semantic segmentation of the seminal vesicle-vas deferens area was performed using a convolutional neural network-based approach. The dataset was split into training and test data in a 10:3 ratio. The average time required by 6 novice urologists to correctly recognize the SV-VD was compared using intraoperative videos with and without segmentation masks generated by the convolutional neural network model, which was evaluated with the test data using the Dice similarity coefficient. Training and test datasets were compared using the Mann–Whitney U-test and chi-square test. Time required to recognize the SV-VD was evaluated using the Mann–Whitney U-test. Results: From 26 patient videos, 1 040 images were created (520 SV-VD annotated images and 520 SV-VD non-displayed images). The convolutional neural network model had a Dice similarity coefficient value of 0.73 in the test data. Compared with original videos, videos with the generated segmentation mask promoted significantly faster seminal vesicle and vas deferens recognition (P < .001). Conclusion: The convolutional neural network model provides accurate recognition of the SV-VD in the posterior approach RARP, which may be helpful, especially for novice urologists.

    DOI: 10.1016/j.urology.2022.12.006

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  19. Database-driven patient-specific registration error compensation method for image-guided laparoscopic surgery Invited Reviewed

    Yuichiro Hayashi, Kazunari Misawa, Kensaku Mori

    International Journal of Computer Assisted Radiology and Surgery   Vol. 18 ( 1 ) page: 63 - 69   2023.1

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    DOI: 10.1007/s11548-022-02804-y

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  20. Computer-Aided Size Estimation of Colorectal Polyps Invited Reviewed

    Hotta K., Itoh H., Mori Y., Misawa M., Mori K., Kudo S.e.

    Techniques and Innovations in Gastrointestinal Endoscopy   Vol. 25 ( 2 ) page: 186 - 188   2023.1

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    Authorship:Corresponding author   Language:English   Publishing type:Research paper (scientific journal)   Publisher:Techniques and Innovations in Gastrointestinal Endoscopy  

    DOI: 10.1016/j.tige.2022.11.004

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  21. Surgical area recognition from laparoscopic images in laparoscopic gastrectomy for gastric cancer using label smoothing and uncertainty Invited Reviewed

    Yuichiro Hayashi,Kazunari Misawa, Kensaku Mori

    Proc. SPIE 12466   Vol. 12466   2023

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    DOI: 10.1117/12.2654775

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  22. A semantic segmentation method for laparoscopic images using semantically similar groups Invited Reviewed

    Uramoto L., Hayashi Y., Oda M., Kitasaka T., Misawa K., Mori K.

    Progress in Biomedical Optics and Imaging - Proceedings of SPIE   Vol. 12466   2023

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    In this paper, we present a segmentation method for laparoscopic images using semantically similar groups for multi-class semantic segmentation. Accurate semantic segmentation is a key problem for computer assisted surgeries. Common segmentation models do not explicitly learn similarities between classes. We propose a model that, in addition to learning to segment an image into classes, also learns to segment it into human-defined semantically similar groups. We modify the LinkNet34 architecture by adding a second decoder with an auxiliary task of segmenting the image into these groups. The feature maps of the second decoder are merged into the final decoder. We validate our method against our base model LinkNet34 and a larger LinkNet50. We find that our proposed modification increased the performance both with mean Dice (average +1.5%) and mean Intersection over Union metrics (average +2.8%) on two laparoscopic datasets.

    DOI: 10.1117/12.2654636

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  23. 膀胱鏡画像におけるtiny-YOLOを用いた腫瘍検出 Invited Reviewed

    牟田口 淳, 小田 昌宏, 猪口 淳一, 森 健策, 江藤 正俊

    生体医工学   Vol. Annual61 ( Abstract ) page: 255_2 - 255_2   2023

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    <p>【背景】膀胱癌は経尿道手術後に再発が多い腫瘍であり、膀胱鏡での腫瘍の見落としが原因とされている。内視鏡での観察は、従来の白色光(WLI)の他に、NBIを使用するが、いずれの腫瘍検出精度は検者の技量・経験に依存するため、検査の再現性・客観性が少ないことが課題である。近年、人工知能(AI)が多くの医療分野で活用されており、AIによる検査は、客観性・再現性を持った上で、エキスパートレベルと同程度の診断能を持つ可能性があるとされている。今回、WLI/NBI膀胱鏡画像を用いて、AIによる腫瘍検出の精度を検証した。【方法】2019年から2021年まで、経尿道的膀胱腫瘍切除術(TURBT)の際に、WLI/NBIを用いて観察を行った症例の手術動画から膀胱鏡画像を作成し、腫瘍を含む画像を腫瘍画像、腫瘍を含まない画像を正常画像と定義した。腫瘍画像内の膀胱腫瘍を矩形でアノテーションを行い、テストデータ用の画像を用いてAIによる感度、特異度、陽性的中率を評価した。AIでの物体検出はtiny-YOLOを用い、腫瘍検出精度の検証を行った。【結果】WLIとNBIから、それぞれ腫瘍画像をそれぞれtiny-YOLOで学習を行い、腫瘍画像(WLI: 525枚、NBI:219枚)と正常画像(WLI:98枚、NBI:108枚)で精度検証を行った。AIによる物体検出の感度/特異度/陽性的中率は、WLIで87.8%/88.8%/97.7%、NBIで82.2%/81.4%/90.0%であった。【結論】膀胱鏡画像において、AIにより比較的良好に腫瘍検出が可能であった。更なる精度改善、リアルタイム検出への課題について、文献的考察を加え報告する。</p>

    DOI: 10.11239/jsmbe.annual61.255_2

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  24. 看護師の職域プレゼンティズム(心身不調)をIoTセンシングで早期検出する Invited Reviewed

    山下 佳子, 大山 慎太郎, 鈴木 輝彦, 坂本 祐二, 出野 義則, 山下 暁士, 赤川 里美, 藤井 晃子, 白鳥 義宗, 森 健策

    生体医工学   Vol. Annual61 ( Abstract ) page: 225_1 - 225_1   2023

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    <p>看護職は心身不調に由来する顕在化していない労働生産性低下状態(プレゼンティズム)が他職種より多く、不規則な勤務形態や強い心身ストレスにより、離職に至る率も高いのが現状である。類似研究は腰痛に注目してウェアラブルデバイスで筋硬度測定によりプレゼンティズム発生を予測するが、性別や運動経験の有無など個人の特性を大きく反映するため予測が難しいと報告がある。そこで本研究では、プレゼンティズムの主な要因で筋骨格系疼痛の原因となる看護行動を、Internet of Things (IoT)センシングで抽出・判別することを目的とする。既設のBLE 屋内測位(AoA法)アンテナを利用した加速度センサデバイスによる看護行動データの収集と、観察者の直接観察による看護行動に対するラベルデータ(看護行動、実施場所、行動の開始・終了時刻を入力)作成を実施した。看護行動は身体プレゼンティズムリスク(腰痛・膝痛・頚肩腕痛)に分類し定義した。1分毎の行動データ(位置情報の平均値、加速度の最大・最小・平均値・標準偏差)、及び時刻を入力データとし、ラベルデータを教師データとして機械学習を行い、行動認識モデルを構築した。看護行動割合は、腰痛43.6%、膝痛3.3%、頚肩腕痛7.4%であった。IoTセンシングで個人ごとのプレゼンティズムに発展する行動をモニタリングすることにより、プレゼンティズムを予防することが可能と示唆された。</p>

    DOI: 10.11239/jsmbe.annual61.225_1

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  25. 医用画像とAI Invited

    森 健策

    カレントテラピー   Vol. 41   page: 79 - 79   2023

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  26. TriMix: A General Framework for Medical Image Segmentation from Limited Supervision Invited Reviewed

    Zheng Z., Hayashi Y., Oda M., Kitasaka T., Mori K.

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)   Vol. 13846 LNCS   page: 185 - 202   2023

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    We present a general framework for medical image segmentation from limited supervision, reducing the reliance on fully and densely labeled data. Our method is simple, jointly trains triple diverse models, and adopts a mix augmentation scheme, and thus is called TriMix. TriMix imposes consistency under a more challenging perturbation, i.e., combining data augmentation and model diversity on the tri-training framework. This straightforward strategy enables TriMix to serve as a strong and general learner learning from limited supervision using different kinds of imperfect labels. We conduct extensive experiments to show TriMix’s generic purpose for semi- and weakly-supervised segmentation tasks. Compared to task-specific state-of-the-arts, TriMix achieves competitive performance and sometimes surpasses them by a large margin. The code is available at https://github.com/MoriLabNU/TriMix.

    DOI: 10.1007/978-3-031-26351-4_12

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  27. Thrombosis region extraction and quantitative analysis in confocal laser scanning microscopic image sequence in in-vivo imaging Invited Reviewed

    Wu Y., Oda M., Hayashi Y., Kawamura S., Takebe T., Mori K.

    Progress in Biomedical Optics and Imaging - Proceedings of SPIE   Vol. 12468   2023

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    In this paper, we propose a scheme that includes automated extraction of thrombus regions and quantitative analysis of thrombosis in confocal laser scanning microscope (CLSM) blood flow image sequence. Making thrombosis model in animal models play an important role in the development of antithrombotic drugs and ascertaining thrombosis mechanisms. Making thrombosis model in cerebral cortex of mice is usually observed using a CLSM in the fluorescence mode. However, some small changes of thrombus regions are not easily observed in CLSM blood flow image sequences. In addition, it is not easy for researchers to quantitatively analyze the degree of thrombosis. Therefore, we propose a scheme to achieve automatic thrombosis region extraction and quantitative analysis. In which, our thrombosis region extraction method uses analysis of changing pattern of thrombosis regions in CLSM blood flow image sequence. Experimental results showed that our scheme can help biological researchers observe and analyze the changes of thrombosis in animal models and reduced the use of fluorescent thrombus markers.

    DOI: 10.1117/12.2654632

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  28. Real Bronchoscopic Images-based Bronchial Nomenclature: a Preliminary Study Invited Reviewed

    Wang C., Hayashi Y., Oda M., Kitasaka T., Takabatake H., Mori M., Honma H., Natori H., Mori K.

    Progress in Biomedical Optics and Imaging - Proceedings of SPIE   Vol. 12466   2023

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    This article describes a method for bronchial nomenclature using real bronchoscopic (RB) images and pre-built knowledge base of branches. The bronchus has a complex tree-like structure, which increases the difficulty of bronchoscopy. Therefore, a bronchoscopic navigation system is used to help physicians during examination. Conventional navigation system used preoperative CT images and real bronchoscopic images to obtain the camera pose for navigation, whose accuracy is influenced by organ deformation. We propose a bronchial nomenclature method to estimate branch names for bronchoscopic navigation. This method consists of a bronchus knowledge base construction model, a camera motion estimation module, an anatomical structure tracking module, and a branch name estimation module. The knowledge base construction module is used to find the relationship of each branch. The anatomical tracking module is used to track the bronchial orifice (BO) extracted in each RB frame. The camera motion estimation module is used to estimate the camera motion between two frames. The branch name estimation module uses the pre-built bronchus knowledge base and BO tracking results to find the name of each branch. Experimental results showed that it is possible to estimate branch names using only RB images and the pre-built knowledge base of branches.

    DOI: 10.1117/12.2654508

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  29. Priority attention network with Bayesian learning for fully automatic segmentation of substantia nigra from neuromelanin MRI Invited Reviewed

    Hu T., Itoh H., Oda M., Saiki S., Hattori N., Kamagata K., Aoki S., Mori K.

    Progress in Biomedical Optics and Imaging - Proceedings of SPIE   Vol. 12464   2023

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    Neuromelanin magnetic resonance imaging (NM-MRI) has been widely used in the diagnosis of Parkinson’s disease (PD) for its significantly enhanced contrast between the PD-related structure, the substantia nigra (SN) and surrounding tissues. To develop the computer-aided diagnosis (CAD) system of PD and reduce the labor burden of clinicians, precise and automatic segmentation of SN is becoming more and more desired. This paper proposes a novel network combining the priority gating attention and Bayesian learning for improving the accuracy of fully automatic SN segmentation from NM-MRI. Different from the conventional gated attention model, the proposed network uses the prior SN probability map for guiding the attention computation and reducing the potential disruptions introduced by the background. Additionally, to lower the risks of over-fitting and estimate the confidence scores for the segmentation results, Bayesian learning with Monte Carlo dropout is applied in the training and testing phases. The quantitative results showed that the proposed network acquired the averaged Dice score of 79.46% in comparison with the baseline model 77.93%.

    DOI: 10.1117/12.2655112

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  30. Octree Cube Constraints in PBD Method for High Resolution Surgical Simulation Invited Reviewed

    Miyazaki R., Hayashi Y., Oda M., Mori K.

    Progress in Biomedical Optics and Imaging - Proceedings of SPIE   Vol. 12464   2023

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    This paper proposes a deformable tissue model that introduces octree lattice vertex layout and cubic constraints to the orthodox PBD (Position Based Dynamics) method. Surgical simulation is expected to provide a safe method for training in surgery, which is especially useful for preoperative education of inexperienced surgeons and/or for the case a prior attempt is required. To build a surgical simulator, it is necessary to develop organ models with deformations and interaction algorithms between surgical instruments and organ models, all of which must be performed in real time. Since existing surgical simulators focus on real-time performance, the resolution of organ models is limited. The proposed method restricts the vertex locations of the PBD method to the vertices of the octree lattice to save computation time while maintaining a high deformation resolution. To obtain appropriate results even for large deformations, three-dimensional constraints are applied to each octree cube as the constraints of the PBD method. In the simulations, we tested the overall deformation by dropping a liver model and the local deformation scene by laparoscopic clipping. As a result, we achieved deformation simulations at 26.5 fps for the model with approximately 2,672 cube elements and 20,659 vertices.

    DOI: 10.1117/12.2654092

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  31. Multi-view Guidance for Self-supervised Monocular Depth Estimation on Laparoscopic Images via Spatio-Temporal Correspondence Invited Reviewed

    Li W., Hayashi Y., Oda M., Kitasaka T., Misawa K., Mori K.

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)   Vol. 14228 LNCS   page: 429 - 439   2023

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    This work proposes an innovative self-supervised approach to monocular depth estimation in laparoscopic scenarios. Previous methods independently predicted depth maps ignoring spatial coherence in local regions and temporal correlation between adjacent images. The proposed approach leverages spatio-temporal coherence to address the challenges of textureless areas and homogeneous colors in such scenes. This approach utilizes a multi-view depth estimation model to guide monocular depth estimation when predicting depth maps. Moreover, the minimum reprojection error is extended to construct a cost volume for the multi-view model using adjacent images. Additionally, a 3D consistency of the point cloud back-projected from predicted depth maps is optimized for the monocular depth estimation model. To benefit from spatial coherence, deformable patch-matching is introduced to the monocular and multi-view models to smooth depth maps in local regions. Finally, a cycled prediction learning for view synthesis and relative poses is designed to exploit the temporal correlation between adjacent images fully. Experimental results show that the proposed method outperforms existing methods in both qualitative and quantitative evaluations. Our code is available at https://github.com/MoriLabNU/MGMDepthL.

    DOI: 10.1007/978-3-031-43996-4_41

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  32. Masked Frequency Consistency for Domain-Adaptive Semantic Segmentation of Laparoscopic Images Invited Reviewed

    Zhao X., Hayashi Y., Oda M., Kitasaka T., Mori K.

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)   Vol. 14220 LNCS   page: 663 - 673   2023

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    Authorship:Corresponding author   Language:English   Publishing type:Research paper (scientific journal)   Publisher:Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)  

    Semantic segmentation of laparoscopic images is an important issue for intraoperative guidance in laparoscopic surgery. However, acquiring and annotating laparoscopic datasets is labor-intensive, which limits the research on this topic. In this paper, we tackle the Domain-Adaptive Semantic Segmentation (DASS) task, which aims to train a segmentation network using only computer-generated simulated images and unlabeled real images. To bridge the large domain gap between generated and real images, we propose a Masked Frequency Consistency (MFC) module that encourages the network to learn frequency-related information of the target domain as additional cues for robust recognition. Specifically, MFC randomly masks some high-frequency information of the image to improve the consistency of the network’s predictions for low-frequency images and real images. We conduct extensive experiments on existing DASS frameworks with our MFC module and show performance improvements. Our approach achieves comparable results to fully supervised learning method on the CholecSeg8K dataset without using any manual annotation. The code is available at github.com/MoriLabNU/MFC.

    DOI: 10.1007/978-3-031-43907-0_63

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  33. Improved method for COVID-19 Classification of Complex-Architecture CNN from Chest CT volumes using Orthogonal Ensemble Networks Invited Reviewed

    Toda R., Oda M., Hayashi Y., Otake Y., Hashimoto M., Akashi T., Aoki S., Mori K.

    Progress in Biomedical Optics and Imaging - Proceedings of SPIE   Vol. 12465   2023

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    This paper introduces the improved method for the COVID-19 classification based on computed tomography (CT) volumes using a combination of a complex-architecture convolutional neural network (CNN) and orthogonal ensemble networks (OEN). The novel coronavirus disease reported in 2019 (COVID-19) is still spreading worldwide. Early and accurate diagnosis of COVID-19 is required in such a situation, and the CT scan is an essential examination. Various computer-aided diagnosis (CAD) methods have been developed to assist and accelerate doctors' diagnoses. Although one of the effective methods is ensemble learning, existing methods combine some major models which do not specialize in COVID-19. In this study, we attempted to improve the performance of a CNN for the COVID-19 classification based on chest CT volumes. The CNN model specializes in feature extraction from anisotropic chest CT volumes. We adopt the OEN, an ensemble learning method considering inter-model diversity, to boost its feature extraction ability. For the experiment, We used chest CT volumes of 1283 cases acquired in multiple medical institutions in Japan. The classification result on 257 test cases indicated that the combination could improve the classification performance.

    DOI: 10.1117/12.2653792

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  34. Classification of COVID-19 cases from chest CT volumes using hybrid model of 3D CNN and 3D MLP-Mixer Invited Reviewed

    Oda M., Zheng T., Hayashi Y., Otake Y., Hashimoto M., Akashi T., Aoki S., Mori K.

    Progress in Biomedical Optics and Imaging - Proceedings of SPIE   Vol. 12465   2023

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    This paper proposes an automated classification method of COVID-19 chest CT volumes using improved 3D MLP-Mixer. Novel coronavirus disease 2019 (COVID-19) spreads over the world, causing a large number of infected patients and deaths. Sudden increase in the number of COVID-19 patients causes a manpower shortage in medical institutions. Computer-aided diagnosis (CAD) system provides quick and quantitative diagnosis results. CAD system for COVID-19 enables efficient diagnosis workflow and contributes to reduce such manpower shortage. In image-based diagnosis of viral pneumonia cases including COVID-19, both local and global image features are important because viral pneumonia cause many ground glass opacities and consolidations in large areas in the lung. This paper proposes an automated classification method of chest CT volumes for COVID-19 diagnosis assistance. MLP-Mixer is a recent method of image classification using Vision Transformer-like architecture. It performs classification using both local and global image features. To classify 3D CT volumes, we developed a hybrid classification model that consists of both a 3D convolutional neural network (CNN) and a 3D version of the MLP-Mixer. Classification accuracy of the proposed method was evaluated using a dataset that contains 1205 CT volumes and obtained 79.5% of classification accuracy. The accuracy was higher than that of conventional 3D CNN models consists of 3D CNN layers and simple MLP layers.

    DOI: 10.1117/12.2654706

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  35. Automated Detection of the Thoracic Ossification of the Posterior Longitudinal Ligament Using Deep Learning and Plain Radiographs. Invited Reviewed

    Ito S, Nakashima H, Segi N, Ouchida J, Oda M, Yamauchi I, Oishi R, Miyairi Y, Mori K, Imagama S

    BioMed research international   Vol. 2023   page: 8495937   2023

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    Ossification of the ligaments progresses slowly in the initial stages, and most patients are unaware of the disease until obvious myelopathy symptoms appear. Consequently, treatment and clinical outcomes are not satisfactory. This study is aimed at developing an automated system for the detection of the thoracic ossification of the posterior longitudinal ligament (OPLL) using deep learning and plain radiography. We retrospectively reviewed the data of 146 patients with thoracic OPLL and 150 control cases without thoracic OPLL. Plain lateral thoracic radiographs were used for object detection, training, and validation. Thereafter, an object detection system was developed, and its accuracy was calculated. The performance of the proposed system was compared with that of two spine surgeons. The accuracy of the proposed object detection model based on plain lateral thoracic radiographs was 83.4%, whereas the accuracies of spine surgeons 1 and 2 were 80.4% and 77.4%, respectively. Our findings indicate that our automated system, which uses a deep learning-based method based on plain radiographs, can accurately detect thoracic OPLL. This system has the potential to improve the diagnostic accuracy of thoracic OPLL.

    DOI: 10.1155/2023/8495937

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  36. AI画像解析による内視鏡外科手術手技のビデオ評価及び手術支援システムの構築 Invited Reviewed

    安井 昭洋, 内田 広夫, 森 健策, 石田 昇平, 出家 亨一, 檜 顕成, 城田 千代栄, 小田 昌宏, 林 雄一郎

    生体医工学   Vol. Annual61 ( Abstract ) page: 127_2 - 127_2   2023

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    <p>【はじめに】術後成長発達する小児患者にとって、低侵襲手術は非常に重要である。しかし患者数は限られているため、しっかりとした手術を行うためにoff the job-training(OJT)が重要である。さらにOJTでの効率的な手技獲得には、手技を客観的に評価しfeed backを行うシステムが必須である。また安全で効率的な内視鏡手術を行うためには、臓器の位置関係の把握が必要であるため、術中ナビゲーションは重要な要件となる。これらの課題に対して、AIを用いた内視鏡手技評価および手術支援システムの構築に着手しており現状の成果を報告する。【方法と結果】食道閉鎖症モデルを用いた吻合手技を被験者に課し、各被験者の手技を最初に人の目で「check 表」「エラー項目」「時間」を用いて評価した。次にビデオから検出した鉗子の動きと人が判定した手技優劣の関係性をAIで学習させ、上位88%・下位95%の精度で手技優劣が自動判定可能となった。この結果を解析することで今まで必要だった50項目以上の肉眼チェックが、わずか7項目チェックするだけで手技の優劣を判断できることが明らかになった。現在食道閉鎖症の手術画像を用いて、食道・迷走神経・気管を深層学習させ、各種構造物の自動認識を進めている。【まとめ】AI画像解析により内視鏡手技の優劣をビデオで判定可能となった。この結果から新たに効率的な手技判断基準を定めることができた。術中ナビゲーションは現在精度のさらなる向上を目指している。</p>

    DOI: 10.11239/jsmbe.annual61.127_2

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  37. Anatomy Aware-based 2.5D Bronchoscope Tracking for Image-guided Bronchoscopic Navigation Invited Reviewed

    Cheng Wang, Masahiro Oda, Yuichiro Hayashi, Takayuki Kitasaka, Hayato Itto, Hirotoshi honma,Hirotsugu takabatake, Masaki Mori, Hiroshi Natori, Kensaku Mori

    Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization     2022.12

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    DOI: 10.1080/21681163.2022.2152728

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  38. Surgical Assistance in Laparoscopic Surgery Using AI Invited Reviewed

    HAYASHI Yuichiro, MORI Kensaku

    Medical Imaging Technology   Vol. 40 ( 4 ) page: 164 - 169   2022.9

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    Authorship:Corresponding author   Language:Japanese   Publishing type:Research paper (scientific journal)   Publisher:The Japanese Society of Medical Imaging Technology  

    <p>Laparoscopic surgery is currently performed as one of surgical procedures for various organs. Laparoscopic surgery requires highly skill of surgeon. Therefore, computer aided surgery which is used computer technology for assisting surgery has been researched. These assistances are sometimes referred as surgical navigation. In recent year, as the development of AI (Artificial Intelligence) technology, there are many researches on laparoscopic video analysis using deep learning for assisting laparoscopic surgery. In this paper, we introduce our researches about laparoscopic video analysis using AI.</p>

    DOI: 10.11409/mit.40.164

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  39. AI for VR Invited Reviewed

    森 健策

    日本VR医学会学術大会プログラム・抄録集   Vol. 2022 ( 0 ) page: 9 - 9   2022.8

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    Authorship:Corresponding author   Language:Japanese   Publishing type:Research paper (scientific journal)   Publisher:日本VR医学会  

    DOI: 10.24764/jsmvr.2022.0_9

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  40. BONE MARROW EDEMA SCORE IN HAND X-RAY FILM BY AI DEEP LEARNING ASSOCIATE WITH MRI BONE EDEMA IN RHEUMATOID ARTHRITIS. Invited Reviewed

    Katayama, K; Pan, D; Oda, M; Okubo, T; Mori, K

    ANNALS OF THE RHEUMATIC DISEASES   Vol. 81   page: 1773 - 1774   2022.6

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    DOI: 10.1136/annrheumdis-2022-eular.933

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  41. 30年間の医用画像研究経験を振り返り未来を考える Invited Reviewed

    森 健策

    情報・システムソサイエティ誌   Vol. 27 ( 1 ) page: 16 - 17   2022.5

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    Authorship:Corresponding author   Language:Japanese   Publishing type:Research paper (scientific journal)   Publisher:一般社団法人電子情報通信学会  

    DOI: 10.1587/ieiceissjournal.27.1_16

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  42. Depth Estimation from Single-shot Monocular Endoscope Image Using Image Domain Adaptation And Edge-Aware Depth Estimation Reviewed

    Masahiro Oda, Hayato Itoh, Kiyohito Tanaka, Hirotsugu Takabatake, Masaki Mori, Hiroshi Natori, Kensaku Mori

    Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization   Vol. 10 ( 3 ) page: 266 - 273   2022.5

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  43. Uncertainty meets 3D-spatial feature in colonoscopic polyp-size determination Invited Reviewed

    Hayato Itoh, Masahiro Oda, Kai Jiang, Yuichi Mori, Masashi Misawa, Shin-Ei Kudo, Kenichiro Imai, Sayo Ito, Kinichi Hotta, Kensaku Mori

    Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization   Vol. 10 ( 3 ) page: 289 - 298   2022.5

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  44. Spatially variant biases considered self-supervised depth estimation based on laparoscopic videos Reviewed

    Wenda Li, Yuichiro Hayashi, Masahiro Oda, Takayuki Kitasaka, Kazunari Misawa, Kensaku Mori

    Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization   Vol. 10 ( 3 ) page: 274 - 282   2022.5

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  45. SR-CycleGAN: super-resolution of clinical CT to micro-CT level with multi-modality super-resolution loss Reviewed

    Tong Zheng, Hirohisa Oda, Yuichiro Hayashi, Takayasu Moriya, Shota Nakamura, Masaki Mori, Hirotsugu Takabatake, Hiroshi Natori, Masahiro Oda, Kensaku Mori

    Journal of Medical Imaging   Vol. 9 ( 2 ) page: 024003-1 - 28   2022.4

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  46. Artificial Intelligence for Computer Vision in Surgery: A Call for Developing Reporting Guidelines. Invited Reviewed

    Kitaguchi D, Watanabe Y, Madani A, Hashimoto DA, Meireles OR, Takeshita N, Mori K, Ito M, Computer Vision in Surgery International Collaborative.

    Annals of surgery   Vol. 275 ( 4 ) page: e609 - e611   2022.4

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    DOI: 10.1097/SLA.0000000000005319

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  47. Evaluation in real-time use of artificial intelligence during colonoscopy to predict relapse of ulcerative colitis: a prospective study Invited Reviewed

    Yasuharu Maeda, Shin-ei Kudo, Noriyuki Ogata, Masashi Misawa, Marietta Iacucci, Mayumi Homma, Tetsuo Nemoto, Kazumi Takishima, Kentaro Mochida, Hideyuki Miyachi, Toshiyuki Baba, Kensaku Mori, Kazuo Ohtsuka, Yuichi Mori

    Gastrointestinal Endoscopy     2022.4

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  48. Artificial Intelligence-Based Total Mesorectal Excision Plane Navigation in Laparoscopic Colorectal Surgery. Invited Reviewed

    Igaki T, Kitaguchi D, Kojima S, Hasegawa H, Takeshita N, Mori K, Kinugasa Y, Ito M

    Diseases of the colon and rectum     2022.2

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    DOI: 10.1097/DCR.0000000000002393

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  49. A cascaded fully convolutional network framework for dilated pancreatic duct segmentation Invited Reviewed

    Chen Shen, Holger R. Roth, Yuichiro Hayashi, Masahiro Oda, Tadaaki Miyamoto, Gen Sato, Kensaku Mori

    International Journal of Computer Assisted Radiology and Surgery   Vol. 17 ( 2 ) page: 343 - 354   2022.2

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    DOI: 10.1007/s11548-021-02530-x

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  50. impact of the clinical use of artificial intelligence-assisted neoplasia detection for colonoscopy: a large-scale prospective, propensity score-matched study (with video) Reviewed

    Misaki Ishiyama, Shin-ei Kudo, Masashi Misawa, Yuichi Mori, Yasuharu Maeda, Katsuro Ichimasa, Toyoki Kudo, Takemasa Hayashi, Kunihiko Wakamura, Hideyuki Miyachi, Fumio Ishida, Hayato Itoh, Masahiro Oda, Kensaku Mori

      Vol. 95 ( 1 ) page: 155 - 163   2022.1

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    DOI: 10.1016/j.gie.2021.07.022

  51. Joint multi organ and tumor segmentation from partial labels using federated learning Invited Reviewed

    Chen Shen, Pochuan Wang, Dong Yang, Daguang Xu, Masahiro Oda, Po-Ting Chen, Kao-Lang Liu, Wei-Chin Liao, Chiou-Shann Fuh, Kensaku Mori , Weichung Wang, Holger R. Roth

    ,LNCS 13573   Vol. 13573   page: 58 - 67   2022

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    DOI: 10.1007/978-3-031-18523-6_6

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  52. Depth-based branching level estimation for bronchoscopic navigation Invited Reviewed

    Cheng Wang, Yuichiro Hayashi, Masahiro Oda, Takayuki Kitasaka, Hirotsugu Takabatake, Masaki Mori, Hirotoshi Honma, Hiroshi Natori , Kensaku Mori

    International Journal of Computer Assisted Radiology and Surgery   Vol. 16 ( 10 ) page: 1795 - 1804   2021.10

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    DOI: 10.1007/s11548-021-02460-8

    DOI: 10.1007/s11548-021-02460-8

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  53. Artificial intelligence and computer-aided diagnosis for colonoscopy: where do we stand now? Invited Reviewed

    Kudo Shin-Ei, Mori Yuichi, Abdel-aal Usama M., Misawa Masashi, Itoh Hayato, Oda Masahiro, Mori Kensaku

    TRANSLATIONAL GASTROENTEROLOGY AND HEPATOLOGY   Vol. 6   page: 64   2021.10

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    Computer-aided diagnosis (CAD) for colonoscopy with use of artificial intelligence (AI) is catching increased attention of endoscopists. CAD allows automated detection and pathological prediction, namely optical biopsy, of colorectal polyps during real-time endoscopy, which help endoscopists avoid missing and/or misdiagnosing colorectal lesions. With the increased number of publications in this field and emergence of the AI medical device that have already secured regulatory approval, CAD in colonoscopy is now being implemented into clinical practice. On the other side, drawbacks and weak points of CAD in colonoscopy have not been thoroughly discussed. In this review, we provide an overview of CAD for optical biopsy of colorectal lesions with a particular focus on its clinical applications and limitations.

    DOI: 10.21037/tgh.2019.12.14

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  54. Deep learning system for automatic detection of bladder tumors in cystoscopic images Invited Reviewed

    Mutaguchi J., Oda M., Ueda S., Kinoshita F., Naganuma H., Matsumoto T., Lee K., Monji K., Kashiwagi K., Takeuchi A., Shiota M., Inokuchi J., Mori K., Eto M.

    EUROPEAN UROLOGY   Vol. 79   page: S1022 - S1023   2021.6

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  55. Development of Diagnosis Assistant AI for COVID-19 Patients Invited Reviewed

    ODA Masahiro, ZHENG Tong, HAYASHI Yuichiro, MORI Kensaku

    Medical Imaging Technology   Vol. 39 ( 1 ) page: 13 - 19   2021.1

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    <p>We introduce AIs for diagnosis assistance of COVID-19 patients from CT volumes that were developed in Nagoya University. Novel coronavirus disease 2019(COVID-19)spreads over the world causing the large number of infected patients and deaths. Diagnosis assistance by AI is effective for diagnosing the large number of patients that are caused by infective diseases. We developed diagnosis assistant AIs for COVID-19 cases that evaluate the typical-ness of COVID-19 case based on image appearances from a CT volume. We developed three essential methods for the AIs including the lung region segmentation method, the clustering method of lung region, and the COVID-19 typical-ness evaluation method. To develop the AIs, we utilized huge number of medical images stored in the cloud platform of medical bigdata. We confirmed our AI has high performance for diagnosis assistance in the evaluations using CT volumes of real COVID-19 patients.</p>

    DOI: 10.11409/mit.39.13

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  56. Automated Detection of Spinal Schwannomas Utilizing Deep Learning Based on Object Detection from MRI Invited Reviewed International coauthorship

    Sadayuki Ito, Kei Ando, Kazuyoshi Kobayashi, Hiroaki Nakashima Masahiro Oda, Masaaki Machino, Shunsuke Kanbara, Taro Inoue, Hidetoshi Yamaguchi, Hiroyuki Koshimizu, Kensaku Mori, Naoki Ishiguro, Shiro Imagama

    Spine   Vol. 46 ( 2 ) page: 95 - 100   2021.1

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    DOI: 10.1097/BRS.0000000000003749

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  57. Depth Estimation from Single-shot Monocular Endoscope Image Using Image Domain Adaptation And Edge-Aware Depth Estimation Invited Reviewed

    Masahiro Oda, Hayato Itoh, Kiyohito Tanaka, Hirotsugu Takabatake, Masaki Mori, Hiroshi Natori, Kensaku Mori

    Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization     2021

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    DOI: 10.1080/21681163.2021.2012835

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  58. Context encoder guided self-supervised siamese depth estimation based on stereo laparoscopic images Invited Reviewed

    Wenda Li, Yuichiro Hayashi, Masahiro Oda, Takayuki Kitasaka, Kazunari Misawa, Kensaku Mori

    Proc. SPIE 11598, Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling   Vol. 11598   2021

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    DOI: 10.1117/12.2582348

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  59. Bronchial orifice segmentation on bronchoscopic video frames based on generative adversarial depth estimation Invited Reviewed

    Cheng Wang, Yuichiro Hayashi, Masahiro Oda, Takayuki Kitasaka, Hirotsugu Takabatake, Masaki Mori, Hirotoshi Honma, Hiroshi Natori, Kensaku Mori

    Proc. SPIE 11598, Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling   Vol. 11598   page: 115980N-1 - 7   2021

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    DOI: 10.1117/12.2582341

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  60. COVID-19 Infection Segmentation from Chest CT Images Based on Scale Uncertainty Invited Reviewed

    Masahiro Oda, Tong Zheng, Yuichiro Hayashi, Yoshito Otake, Masahiro Hashimoto, Toshiaki Akashi, Shigeki Aoki, Kensaku Mori

    LNCS12969   Vol. 12969 LNCS   page: 88 - 97   2021

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    DOI: 10.1007/978-3-030-90874-4_9

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  61. Attention-guided pancreatic duct segmentation from abdominal CT volumes Invited Reviewed

    Chen Shen, Holger Roth, Yuichiro Hayashi, Masahiro Oda, Takaaki Miyamoto, Gen Sato, Kensaku Mori

    LNCS12969   Vol. 12969 LNCS   page: 46 - 55   2021

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    DOI: 10.1007/978-3-030-90874-4_5

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  62. CURRENT STATUS AND FUTURE PERSPECTIVE ON ARTIFICIAL INTELLIGENCE FOR LOWER ENDOSCOPY Invited Reviewed

    MISAWA Masashi, KUDO Shin-ei, MORI Yuichi, MAEDA Yasuharu, OGAWA Yushi, ICHIMASA Katsuro, KUDO Toyoki, WAKAMURA Kunihiko, HAYASHI Takemasa, MIYACHI Hideyuki, BABA Toshiyuki, ISHIDA Fumio, ITOH Hayato, ODA Masahiro, MORI Kensaku

    GASTROENTEROLOGICAL ENDOSCOPY   Vol. 63 ( 7 ) page: 1402 - 1416   2021

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    Authorship:Corresponding author   Language:Japanese   Publishing type:Research paper (scientific journal)   Publisher:Japan Gastroenterological Endoscopy Society  

    <p>The global incidence and mortality rate of colorectal cancer remains high. Colonoscopy is regarded as the gold standard examination for detecting and eradicating neoplastic lesions. However, there are some uncertainties in colonoscopy practice that are related to limitations in human performance. First, approximately one-fourth of colorectal neoplasms are missed on a single colonoscopy. Second, it is still difficult for nonexperts to perform adequately regarding optical biopsy. Third, recording of some quality indicators (e.g. cecal intubation, bowel preparation, and withdrawal speed) which are related to adenoma detection rate, is sometimes incomplete. With recent improvements in machine learning techniques and advances in computer performance, artificial intelligence-assisted computer-aided diagnosis is being increasingly utilized by endoscopists. In particular, the emergence of deep-learning, data-driven machine learning techniques have made the development of computer-aided systems easier than that of conventional machine learning techniques, the former currently being considered the standard artificial intelligence engine of computer-aided diagnosis by colonoscopy. To date, computer-aided detection systems seem to have improved the rate of detection of neoplasms. Additionally, computer-aided characterization systems may have the potential to improve diagnostic accuracy in real-time clinical practice. Furthermore, some artificial intelligence-assisted systems that aim to improve the quality of colonoscopy have been reported. The implementation of computer-aided system clinical practice may provide additional benefits such as helping in educational poorly performing endoscopists and supporting real-time clinical decision-making. In this review, we have focused on computer-aided diagnosis during colonoscopy reported by gastroenterologists and discussed its status, limitations, and future prospects.</p>

    DOI: 10.11280/gee.63.1402

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  63. Clinical and Genetic Characteristics of 18 Patients from 13 Japanese Families with CRX-associated retinal disorder: Identification of Genotype-phenotype Association Invited Reviewed

    Fujinami-Yokokawa Y., Fujinami K., Kuniyoshi K., Hayashi T., Ueno S., Mizota A., Shinoda K., Arno G., Pontikos N., Yang L., Liu X., Sakuramoto H., Katagiri S., Mizobuchi K., Kominami T., Terasaki H., Nakamura N., Kameya S., Yoshitake K., Miyake Y., Kurihara T., Tsubota K., Miyata H., Iwata T., Tsunoda K., Nishimura T., Hayashizaki Y., Kondo M., Shimozawa N., Horiguchi M., Yamamoto S., Kuze M., Naoi N., Machida S., Shimada Y., Nakamura M., Fujikado T., Hotta Y., Takahashi M., Mochizuki K., Murakami A., Kondo H., Ishida S., Nakazawa M., Hatase T., Matsunaga T., Maeda A., Noda K., Tanikawa A., Yamamoto S., Yamamoto H., Araie M., Aihara M., Nakazawa T., Sekiryu T., Kashiwagi K., Kosaki K., Piero C., Fukuchi T., Hayashi A., Hosono K., Mori K., Tanaka K., Furuya K., Suzuki K., Kohata R., Yanagi Y., Minegishi Y., Iejima D., Suga A., Rossmiller B.P., Pan Y., Oshima T., Nakayama M., Teruyama Y., Yamamoto M., Minematsu N., Sanbe H., Mori D., Kijima Y., Mawatari G., Kurata K., Yamada N., Itoh M., Kawaji H., Murakawa Y.

    Scientific Reports   Vol. 10 ( 1 )   2020.12

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    Inherited retinal disorder (IRD) is a leading cause of blindness, and CRX is one of a number of genes reported to harbour autosomal dominant (AD) and recessive (AR) causative variants. Eighteen patients from 13 families with CRX-associated retinal disorder (CRX-RD) were identified from 730 Japanese families with IRD. Ophthalmological examinations and phenotype subgroup classification were performed. The median age of onset/latest examination was 45.0/62.5 years (range, 15–77/25–94). The median visual acuity in the right/left eye was 0.52/0.40 (range, −0.08–2.00/−0.18–1.70) logarithm of the minimum angle of resolution (LogMAR) units. There was one family with macular dystrophy, nine with cone-rod dystrophy (CORD), and three with retinitis pigmentosa. In silico analysis of CRX variants was conducted for genotype subgroup classification based on inheritance and the presence of truncating variants. Eight pathogenic CRX variants were identified, including three novel heterozygous variants (p.R43H, p.P145Lfs*42, and p.P197Afs*22). A trend of a genotype-phenotype association was revealed between the phenotype and genotype subgroups. A considerably high proportion of CRX-RD in ADCORD was determined in the Japanese cohort (39.1%), often showing the mild phenotype (CORD) with late-onset disease (sixth decade). Frequently found heterozygous missense variants located within the homeodomain underlie this mild phenotype. This large cohort study delineates the disease spectrum of CRX-RD in the Japanese population.

    DOI: 10.1038/s41598-020-65737-z

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  64. Clinical characteristics in patients with ossification of the posterior longitudinal ligament: A prospective multi-institutional cross-sectional study Invited Reviewed

    Hirai T., Yoshii T., Ushio S., Mori K., Maki S., Katsumi K., Nagoshi N., Takeuchi K., Furuya T., Watanabe K., Nishida N., Watanabe K., Kaito T., Kato S., Nagashima K., Koda M., Ito K., Imagama S., Matsuoka Y., Wada K., Kimura A., Ohba T., Katoh H., Matsuyama Y., Ozawa H., Haro H., Takeshita K., Watanabe M., Matsumoto M., Nakamura M., Yamazaki M., Okawa A., Kawaguchi Y.

    Scientific Reports   Vol. 10 ( 1 )   2020.12

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    Ossification of the posterior longitudinal ligament (OPLL) can occur throughout the entire spine and can sometimes lead to spinal disorder. Although patients with OPLL sometimes develop physical limitations because of pain, the characteristics of pain and effects on activities of daily living (ADL) have not been precisely evaluated in OPLL patients. Therefore, we conducted a multi-center prospective study to assess whether the symptoms of cervical OPLL are different from those of cervical spondylosis (CS). A total of 263 patients with a diagnosis of cervical OPLL and 50 patients with a diagnosis of CS were enrolled and provided self-reported outcomes, including responses to the Japanese Orthopaedic Association (JOA) Cervical Myelopathy Evaluation Questionnaire (JOACMEQ), JOA Back Pain Evaluation Questionnaire (JOABPEQ), visual analog scale (VAS), and SF-36 scores. The severity of myelopathy was significantly correlated with each domain of the JOACMEQ and JOABPEQ. There was a negative correlation between the VAS score for each domain and the JOA score. There were significantly positive correlations between the JOA score and the Mental Health, Bodily Pain, Physical Functioning, Role Emotional, and Role Physical domains of the SF-36. One-to-one matching resulted in 50 pairs of patients with OPLL and CS. Although there was no significant between-group difference in scores in any of the domains of the JOACMEQ or JOABPEQ, the VAS scores for pain or numbness in the buttocks or limbs were significantly higher in the CS group; however, there was no marked difference in low back pain, chest tightness, or numbness below the chest between the two study groups. The scores for the Role Physical and Body Pain domains of the SF-36 were significantly higher in the OPLL group than in the CS group, and the mean scores for the other domains was similar between the two groups. The results of this study revealed that patients with OPLL were likely to have neck and low back pain and restriction in ADL. No specific type of pain was found in patients with OPLL when compared with those who had CS.

    DOI: 10.1038/s41598-020-62278-3

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  65. Synthetic laparoscopic video generation for machine learning-based surgical instrument segmentation from real laparoscopic video and virtual surgical instruments Reviewed International coauthorship

    Takuya Ozawa,Yuichiro Hayashi,Hirohisa Oda, Masahiro Oda, Takayuki Kitasaka, Nobuyoshi Takeshita, Masaaki Ito, Kensaku Mori

    Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization     2020.11

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    DOI: https://doi.org/10.1080/21681163.2020.1835560

  66. Clinical impact of Endoscopic Surgical Skill Qualification System (ESSQS) by Japan Society for Endoscopic Surgery (JSES) for laparoscopic distal gastrectomy and low anterior resection based on the National Clinical Database (NCD) registry. Invited Reviewed

    Akagi T, Endo H, Inomata M, Yamamoto H, Mori T, Kojima K, Kuroyanagi H, Sakai Y, Nakajima K, Shiroshita H, Etoh T, Saida Y, Yamamoto S, Hasegawa H, Ueno H, Kakeji Y, Miyata H, Kitagawa Y, Watanabe M

    Annals of gastroenterological surgery   Vol. 4 ( 6 ) page: 721 - 734   2020.11

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    DOI: 10.1002/ags3.12384

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  67. A visual SLAM-based bronchoscope tracking scheme for bronchoscopic navigation Invited Reviewed

    Cheng Wang, Masahiro Oda, Yuichiro Hayashi, Benjamin Villard, Takayuki Kitasaka, Hirotsugu Takabatake, Masaki Mori, Hirotoshi Honma, Hiroshi Natori, Kensaku Mori

    International Journal of Computer Assisted Radiology and Surgery   Vol. 15 ( 10 ) page: 1619 - 1630   2020.10

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    DOI: 10.1007/s11548-020-02241-9

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  68. Station number assignment to abdominal lymph node for assisting gastric cancer surgery Reviewed

    Yuichiro Hayashi, Kazunari Misawa, Kensaku Mori

    Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization     2020.10

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    DOI: https://doi.org/10.1080/21681163.2020.1835543

  69. A deformable model for navigated laparoscopic gastrectomy based on finite elemental method Invited Reviewed

    Chen Tao, Wei Guodong, Xu Lili, Shi Weili, Xu Yikai, Zhu Yongyi, Hayashi Yuichiro, Oda Hirohisa, Oda Masahiro, Hu Yanfeng, Yu Jiang, Jiang Zhengang, Li Guoxin, Mori Kensaku

    MINIMALLY INVASIVE THERAPY & ALLIED TECHNOLOGIES   Vol. 29 ( 4 ) page: 210 - 216   2020.7

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    Background: Accurate registration for surgical navigation of laparoscopic surgery is highly challenging due to vessel deformation. Here, we describe the design of a deformable model with improved matching accuracy by applying the finite element method (FEM). Material and methods: ANSYS software was used to simulate an FEM model of the vessel after pull-up based on laparoscopic gastrectomy requirements. The central line of the FEM model and the central line of the ground truth were drawn and compared. Based on the material and parameters determined from the animal experiment, a perigastric vessel FEM model of a gastric cancer patient was created, and its accuracy in a laparoscopic gastrectomy surgical scene was evaluated. Results: In the animal experiment, the FEM model created with Ogden foam material exhibited better results. The average distance between the two central lines was 6.5mm, and the average distance between their closest points was 3.8 mm. In the laparoscopic gastrectomy surgical scene, the FEM model and the true artery deformation demonstrated good coincidence. Conclusion: In this study, a deformable vessel model based on FEM was constructed using preoperative CT images to improve matching accuracy and to supply a reference for further research on deformation matching to facilitate laparoscopic gastrectomy navigation.

    DOI: 10.1080/13645706.2019.1625926

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  70. Automated laparoscopic colorectal surgery workflow recognition using artificial intelligence: Experimental research Invited Reviewed

    Kitaguchi Daichi, Takeshita Nobuyoshi, Matsuzaki Hiroki, Oda Tatsuya, Watanabe Masahiko, Mori Kensaku, Kobayashi Etsuko, Ito Masaaki

    INTERNATIONAL JOURNAL OF SURGERY   Vol. 79   page: 88 - 94   2020.7

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    Background: Identifying laparoscopic surgical videos using artificial intelligence (AI) facilitates the automation of several currently time-consuming manual processes, including video analysis, indexing, and video-based skill assessment. This study aimed to construct a large annotated dataset comprising laparoscopic colorectal surgery (LCRS) videos from multiple institutions and evaluate the accuracy of automatic recognition for surgical phase, action, and tool by combining this dataset with AI. Materials and methods: A total of 300 intraoperative videos were collected from 19 high-volume centers. A series of surgical workflows were classified into 9 phases and 3 actions, and the area of 5 tools were assigned by painting. More than 82 million frames were annotated for a phase and action classification task, and 4000 frames were annotated for a tool segmentation task. Of these frames, 80% were used for the training dataset and 20% for the test dataset. A convolutional neural network (CNN) was used to analyze the videos. Intersection over union (IoU) was used as the evaluation metric for tool recognition. Results: The overall accuracies for the automatic surgical phase and action classification task were 81.0% and 83.2%, respectively. The mean IoU for the automatic tool segmentation task for 5 tools was 51.2%. Conclusions: A large annotated dataset of LCRS videos was constructed, and the phase, action, and tool were recognized with high accuracy using AI. Our dataset has potential uses in medical applications such as automatic video indexing and surgical skill assessments. Open research will assist in improving CNN models by making our dataset available in the field of computer vision.

    DOI: 10.1016/j.ijsu.2020.05.015

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  71. Artificial intelligence for magnifying endoscopy, endocytoscopy, and confocal laser endomicroscopy of the colorectum Invited Reviewed

    Mori, Y; Kudo, SE; Misawa, M; Itoh, H; Oda, M; Mori, K

    TECHNIQUES AND INNOVATIONS IN GASTROINTESTINAL ENDOSCOPY   Vol. 22 ( 2 ) page: 56 - 60   2020.4

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    Because magnifying endoscopy is considered to be more accurate at predicting the histology of colorectal polyps than nonmagnifying endoscopy, it has been attracting a lot of attention, especially in Japan. However, use of magnifying endoscopy is not yet widespread because of its limited availability and the difficulty in interpreting the acquired images. Application of artificial intelligence (AI) is now changing this situation because it helps less-skilled endoscopists to accurately interpret magnified images. Research in this field initially focused on magnifying endoscopy with narrow-band imaging as the target of AI. Most previously published retrospective studies have reported over 90% sensitivity in differentiation of neoplastic lesions; however, automatically indicating the region of interest (ROI) of the polyps that AI should analyze has been found to be challenging. To address this practical problem, some researchers have started to adopt contact endomicroscopy as a target for AI. Contact endomicroscopy includes endocytoscopy (520-fold magnification, Olympus, Tokyo, Japan) and confocal laser endomicroscopy (1000-fold magnification, Mauna Kea, Paris, France). These forms of contact endomicroscopy provide ultramagnified images that make it unnecessary to manually select the ROI because the entire image acquired by contact endomicroscopy is the ROI of the targeted polyps. This strength of contact endomicroscopy has contributed to early implementation of this technology into clinical practice, which may change the utility of magnifying endoscopy in clinical settings and help increase its use globally in the near future.

    DOI: 10.1016/j.tgie.2019.150632

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  72. Clinical application of a surgical navigation system based on virtual thoracoscopy for lung cancer patients: real time visualization of area of lung cancer before induction therapy and optimal resection line for obtaining a safe surgical margin during surgery Invited Reviewed

    Nakamura Shota, Hayashi Yuichiro, Kawaguchi Koji, Fukui Takayuki, Hakiri Shuhei, Ozeki Naoki, Mori Shunsuke, Goto Masaki, Mori Kensaku, Yokoi Kohei

    JOURNAL OF THORACIC DISEASE   Vol. 12 ( 3 ) page: 672 - 679   2020.3

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    Background: We have developed a surgical navigation system that presents virtual thoracoscopic images using computed tomography (CT) image data, as if you are observing intra-thoracic cavity in synchronization with the real thoracoscopic view. Using this system, we made it possible to simultaneously visualize the ‘area of lung cancer before induction therapy’ and the ‘optimal resection line for obtaining a safe surgical margin’ as a virtual thoracoscopic view. We applied this navigation system in the clinical setting in operations for lung cancer patients with chest wall invasion after induction chemoradiotherapy. Methods: The proposed surgical navigation system consisted of a three-dimensional (3D) positional tracker and a virtual thoracoscopy system. The 3D positional tracker was used to recognize the positional information of the real thoracoscope. The virtual thoracoscopy system generated virtual thoracoscopic views based on CT image data. Combined with these two technologies, patient-to-image registration was performed in two patients, and the results generated a virtual thoracoscopic view that was synchronized with the real thoracoscopic view. Results: The operations were started with video-assisted thoracic surgery (VATS), and the navigation system was activated at the same time. The virtual thoracoscopic view was synchronized with the real thoracoscopic view, which also simultaneously indicated the ‘area of lung cancer before induction therapy’ and the ‘optimal resection lines for obtaining a safe surgical margin’. We marked the optimal lines using an electric scalpel, and then performed lobectomy and chest wall resection with a sufficient surgical margin using these landmarks. Pathological examinations confirmed that the surgical margin was negative. No complications related to the navigation system were encountered during or after the procedures. Conclusions: Using this proposed navigation system, we could obtain a ‘CT-derived virtual intrathoracic 3D view of the patient’ that was aligned with the thoracoscopic view during surgery. The accurate identification of areas of cancer invasion before induction therapy using this system might be a useful for determining optimal surgical resection lines.

    DOI: 10.21037/jtd.2019.12.108

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  73. Tensor-cut: A tensor-based graph-cut blood vessel segmentation method and its application to renal artery segmentation Reviewed International coauthorship

    Chenglong Wang, Masahiro Oda, Yuichiro Hayashi, Yasushi Yoshino, Tokunori Yamamoto, Alejandro F. Frangi, Kensaku Mori

    Medical Image Analysis   Vol. 60   page: 101623   2020.2

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    DOI: 10.1016/j.media.2019.101623

  74. [Pharmacological action and clinical effect of tedizolid phosphate (SIVEXTRO<sup>®</sup> Tablets 200 mg, for iv infusion 200 mg), a novel oxazolidinone-class antibacterial drug]. Invited Reviewed

    Mori M, Takase A

    Nihon yakurigaku zasshi. Folia pharmacologica Japonica   Vol. 155 ( 5 ) page: 332 - 339   2020

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    DOI: 10.1254/fpj.20013

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  75. 3Dプリンティングの最新動向 Invited

    森 健策

    インナービジョン   Vol. 35   page: 36 - 37   2020

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  76. Automated eye disease classification method from anterior eye image using anatomical structure focused image classification technique Invited Reviewed

    Oda Masahiro, Yamaguchi Takefumi, Fukuoka Hideki, Ueno Yuta, Mori Kensaku

    MEDICAL IMAGING 2020: COMPUTER-AIDED DIAGNOSIS   Vol. 11314   2020

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    This paper presents an automated classification method of infective and non-infective diseases from anterior eye images. Treatments for cases of infective and non-infective diseases are different. Distinguishing them from anterior eye images is important to decide a treatment plan. Ophthalmologists distinguish them empirically. Quantitative classification of them based on computer assistance is necessary. We propose an automated classification method of anterior eye images into cases of infective or non-infective disease. Anterior eye images have large variations of the eye position and brightness of illumination. This makes the classification difficult. If we focus on the cornea, positions of opacified areas in the corneas are different between cases of the infective and non-infective diseases. Therefore, we solve the anterior eye image classification task by using an object detection approach targeting the cornea. This approach can be said as "anatomical structure focused image classification". We use the YOLOv3 object detection method to detect corneas of infective disease and corneas of non-infective disease. The detection result is used to define a classification result of an image. In our experiments using anterior eye images, 88.3% of images were correctly classified by the proposed method.

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  77. Automated Pancreas Segmentation Using Multi-institutional Collaborative Deep Learning Invited Reviewed

    Wang P., Shen C., Roth H.R., Yang D., Xu D., Oda M., Misawa K., Chen P.T., Liu K.L., Liao W.C., Wang W., Mori K.

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)   Vol. 12444 LNCS   page: 192 - 200   2020

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    The performance of deep learning based methods strongly relies on the number of datasets used for training. Many efforts have been made to increase the data in the medical image analysis field. However, unlike photography images, it is hard to generate centralized databases to collect medical images because of numerous technical, legal, and privacy issues. In this work, we study the use of federated learning between two institutions in a real-world setting to collaboratively train a model without sharing the raw data across national boundaries. We quantitatively compare the segmentation models obtained with federated learning and local training alone. Our experimental results show that federated learning models have higher generalizability than standalone training.

    DOI: 10.1007/978-3-030-60548-3_19

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  78. Abdominal artery segmentation method from CT volumes using fully convolutional neural network Invited Reviewed

    Masahiro Oda, Holger R. Roth, Takayuki Kitasaka, Kazunari Misawa, Michitaka Fujiwara, Kensaku Mori,

    International Journal of Computer Assisted Radiology and Surgery   Vol. 14 ( 12 ) page: 2069 - 2081   2019.12

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    DOI: 10.1007/s11548-019-02062-5

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  79. A view of three dimensional unit structures of alveoli in peripheral lung Invited Reviewed

    Natori Hiroshi, Takabatake Hirotsugu, Mori Masaki, Oda Masahiro, Mori Kensaku, Koba Hiroyuki, Takahashi Hiroki

    EUROPEAN RESPIRATORY JOURNAL   Vol. 54   2019.9

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    DOI: 10.1183/13993003.congress-2019.PA3168

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  80. ARTIFICIAL INTELLIGENCE-ASSISTED POLYP DETECTION SYSTEM FOR COLONOSCOPY, BASED ON THE LARGEST AVAILABLE COLLECTION OF CLINICAL VIDEO DATA FOR MACHINE LEARNING Invited Reviewed

    Misawa Masashi, Kudo Shinei, Mori Yuichi, Cho Tomonari, Kataoka Shinichi, Maeda Yasuharu, Ogawa Yushi, Takeda Kenichi, Nakamura Hiroki, Ichimasa Katsuro, Toyoshima Naoya, Ogata Noriyuki, Kudo Toyoki, Hisayuki Tomokazu, Hayashi Takemasa, Wakamura Kunihiko, Baba Toshiyuki, Ishida Fumio, Itoh Hayato, Oda Masahiro, Mori Kensaku

    GASTROINTESTINAL ENDOSCOPY   Vol. 89 ( 6 ) page: AB646 - AB647   2019.6

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  81. Development of a new laparoscopic detection system for gastric cancer using near-infrared light-emitting clips with glass phosphor Invited Reviewed

    Inada S., Nakanishi H., Oda M., Mori K., Ito A., Hasegawa J., Misawa K., Fuchi S.

    Micromachines   Vol. 10 ( 2 )   2019.1

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    Laparoscopic surgery is now a standard treatment for gastric cancer. Currently, the location of the gastric cancer is identified during laparoscopic surgery via the preoperative endoscopic injection of charcoal ink around the primary tumor; however, the wide spread of injected charcoal ink can make it difficult to accurately visualize the specific site of the tumor. To precisely identify the locations of gastric tumors, we developed a fluorescent detection system comprising clips with glass phosphor (Yb 3+ , Nd 3+ doped to Bi 2 O 3 -B 2 O 3 -based glasses, size: 2 mm × 1 mm × 3 mm) fixed in the stomach and a laparoscopic fluorescent detection system for clip-derived near-infrared (NIR) light (976 nm). We conducted two ex vivo experiments to evaluate the performance of this fluorescent detection system in an extirpated pig stomach and a freshly resected human stomach and were able to successfully detect NIR fluorescence emitted from the clip in the stomach through the stomach wall by the irradiation of excitation light (λ: 808 nm). These results suggest that the proposed combined NIR light-emitting clip and laparoscopic fluorescent detection system could be very useful in clinical practice for accurately identifying the location of a primary gastric tumor during laparoscopic surgery.

    DOI: 10.3390/mi10020081

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  82. Automated Hand Eye Calibration in Laparoscope Holding Robot for Robot Assisted Surgery Invited Reviewed

    Jiang Shuai, Hayashi Yuichiro, Wang Cheng, Oda Masahiro, Kitasaka Takayuki, Misawa Kazunari, Mori Kensaku

    INTERNATIONAL WORKSHOP ON ADVANCED IMAGE TECHNOLOGY (IWAIT) 2019   Vol. 11049   2019

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    DOI: 10.1117/12.2521618

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  83. Colonoscope tracking method based on shape estimation network Invited Reviewed

    Oda Masahiro, Roth Holger R., Kitasaka Takayuki, Furukawa Kazuhiro, Miyahara Ryoji, Hirooka Yoshiki, Navab Nassir, Mori Kensaku

    MEDICAL IMAGING 2019: IMAGE-GUIDED PROCEDURES, ROBOTIC INTERVENTIONS, AND MODELING   Vol. 10951   2019

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    This paper presents a colonoscope tracking method utilizing a colon shape estimation method. CT colonography is used as a less-invasive colon diagnosis method. If colonic polyps or early-stage cancers are found, they are removed in a colonoscopic examination. In the colonoscopic examination, understanding where the colonoscope running in the colon is difficult. A colonoscope navigation system is necessary to reduce overlooking of polyps. We propose a colonoscope tracking method for navigation systems. Previous colonoscope tracking methods caused large tracking errors because they do not consider deformations of the colon during colonoscope insertions. We utilize the shape estimation network (SEN), which estimates deformed colon shape during colonoscope insertions. The SEN is a neural network containing long short-term memory (LSTM) layer. To perform colon shape estimation suitable to the real clinical situation, we trained the SEN using data obtained during colonoscope operations of physicians. The proposed tracking method performs mapping of the colonoscope tip position to a position in the colon using estimation results of the SEN. We evaluated the proposed method in a phantom study. We confirmed that tracking errors of the proposed method was enough small to perform navigation in the ascending, transverse, and descending colons.

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  84. Automatic segmentation of eyeball structures from micro-CT images based on sparse annotation Invited Reviewed

    Takaaki Sugino, Holger Roth, Masahiro Oda, Seiji Omata, Shinya Sakuma, Fumihito Arai, Kensaku Mori

    Proc. SPIE 10578, Medical Imaging 2018   Vol. 10578   page: 105780V-1-105780V-6   2018

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    DOI: 10.1117/12.2293431

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  85. Cascade classification of lesions for automated pathological diagnosis Invited Reviewed

    Hayato Ito, Yuichi Mori, Masashi Misawa, Masahiro Oda, Shin-ei Kudo, Kensaku Mori

    Proc. SPIE 10575, Medical Imaging 2018   Vol. 10575   page: 1057516-1-1057516-6   2018

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    DOI: 10.1117/12.2293495

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  86. 3Dプリンタの最新動向 Invited

    森 健策

    インナービジョン   Vol. 33   page: 35 - 36   2018

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  87. BESNet: Boundary-Enhanced Segmentation of Cells in Histopathological Images Invited Reviewed

    Oda Hirohisa, Roth Holger R., Chiba Kosuke, Sokolic Jure, Kitasaka Takayuki, Oda Masahiro, Hinoki Akinari, Uchida Hiroo, Schnabel Julia A., Mori Kensaku

    MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2018, PT II   Vol. 11071   page: 228 - 236   2018

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    DOI: 10.1007/978-3-030-00934-2_26

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  88. Develop and Validate a Finite Element Method Model for Deformation Matching of Laparoscopic Gastrectomy Navigation Invited Reviewed

    Chen Tao, Wei Guodong, Shi Weili, Hayashi Yuichiro, Oda Masahiro, Jiang Zhengang, Li Guoxin, Mori Kensaku

    MEDICAL IMAGING 2018: IMAGE-GUIDED PROCEDURES, ROBOTIC INTERVENTIONS, AND MODELING   Vol. 10576   2018

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    DOI: 10.1117/12.2293288

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  89. Deep Learning and Its Application to Medical Image Segmentation Invited Reviewed

    ROTH Holger R., SHEN Chen, ODA Hirohisa, ODA Masahiro, HAYASHI Yuichiro, MISAWA Kazunari, MORI Kensaku

    Medical Imaging Technology   Vol. 36 ( 2 ) page: 63 - 71   2018

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    One of the most common tasks in medical imaging is semantic segmentation. Achieving this segmentation automatically has been an active area of research, but the task has been proven very challenging due to the large variation of anatomy across different patients. However, recent advances in deep learning have made it possible to significantly improve the performance of image recognition and semantic segmentation methods in the field of computer vision. Due to the data driven approaches of hierarchical feature learning in deep learning frameworks, these advances can be translated to medical images without much difficulty. Several variations of deep convolutional neural networks have been successfully applied to medical images. Especially fully convolutional architectures have been proven efficient for segmentation of 3D medical images. In this article, we describe how to build a 3D fully convolutional network (FCN) that can process 3D images in order to produce automatic semantic segmentations. The model is trained and evaluated on a clinical computed tomography (CT) dataset and shows stateof-the-art performance in multi-organ segmentation.

    DOI: 10.11409/mit.36.63

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  90. Colon Shape Estimation Method for Colonoscope Tracking Using Recurrent Neural Networks Invited Reviewed

    Oda Masahiro, Roth Holger R., Kitasaka Takayuki, Furukawa Kasuhiro, Miyahara Ryoji, Hirooka Yoshiki, Goto Hidemi, Navab Nassir, Mori Kensaku

    MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2018, PT IV   Vol. 11073   page: 176 - 184   2018

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    DOI: 10.1007/978-3-030-00937-3_21

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  91. Airway Segmentation from 3D Chest CT Volumes Based on Volume of Interest Using Gradient Vector Flow Invited Reviewed

    MENG Qier, KITASAKA Takayuki, ODA Masahiro, UENO Junji, MORI Kensaku

    Medical Imaging Technology   Vol. 36 ( 3 ) page: 133 - 146   2018

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    Authorship:Corresponding author   Language:Japanese   Publishing type:Research paper (scientific journal)   Publisher:The Japanese Society of Medical Imaging Technology  

    In this paper, we propose a new airway segmentation algorithm from 3D chest CT volumes based on the volume of interest (VOI). The algorithm segments each bronchial branch by recognizing the airway regions from the trachea using the VOIs to segment each branch. A VOI is placed to envelop the branch currently being processed. Then a cavity enhancement filter is performed only inside the current VOI so that each branch is extracted. At the same time, we perform a leakage detection scheme to avoid any leakage regions inside the VOI. Next the gradient vector flow magnitude map and a tubular-likeness function are computed in each VOI. This assists the predictions of both the position and direction of the next child VOIs to detect the next child branches to continue the tracking algorithm. Finally, we unify all of the extracted airway regions to form a complete airway tree. We used a dataset that includes 50 standard-dose human chest CT volumes to evaluate our proposed algorithm. The average extraction rate was approximately 78.1% with a significantly decreased false positive rate compared to the previous method.

    DOI: 10.11409/mit.36.133

    CiNii Research

  92. <b>3Dプリンターの基礎と医療応用</b> Invited Reviewed

    森 健策

    心臓   Vol. 49 ( 11 ) page: 1104 - 1113   2017.11

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    Authorship:Corresponding author   Language:Japanese   Publishing type:Research paper (scientific journal)   Publisher:公益財団法人 日本心臓財団  

    DOI: 10.11281/shinzo.49.1104

    CiNii Research

  93. Automated mediastinal lymph node detection from CT volumes based on intensity targeted radial structure tensor analysis Invited

    Hirohisa Oda, Kanwal K. Bhatia, Masahiro Oda, Takayuki Kitasaka, Shingo Iwano, Hirotoshi Homma, Hirotsugu Takabatake, Masaki Mori, Hiroshi Natori, Julia A. Schnabel, Kensaku Mori

    Journal of Medical Imaging   Vol. 4 ( 04 ) page: 1   2017.11

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    DOI: 10.1117/1.jmi.4.4.044502

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  94. Automatic segmentation of head anatomical structures from sparsely-Annotated images Invited Reviewed

    Sugino T., Roth H.R., Eshghi M., Oda M., Chung M.S., Mori K.

    2017 IEEE International Conference on Cyborg and Bionic Systems, CBS 2017   Vol. 2018-January   page: 145 - 149   2017.7

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    Bionic humanoid systems, which are elaborate human models with sensors, have been developed as a tool for quantitative evaluation of doctors' psychomotor skills and medical device performances. For creation of the elaborate human models, this study presents automated segmentation of head sectioned images using sparsely-Annotated data based on deep convolutional neural network. We applied the following fully convolutional networks (FCNs) to the sparse-Annotation-based segmentation: a standard FCN and a dilated convolution based FCN. To validate the availability of FCNs for segmentation of head structures from sparse annotation, we performed 8- and 243-label segmentation experiments using different two sets of head sectioned images in the Visible Korean Human project. In the segmentation experiments, only 10% of all images in each data set were used for training data. Both of the FCNs could achieve the mean segmentation accuracy of more than 85% in the 8-label segmentation. In the 243-label segmentation, though the mean segmentation accuracy was about 50%, the results suggested that the FCNs, especially the dilated convolution based FCNs, had potential to achieve accurate segmentation of anatomical structures, except for small-sized and complex-shaped tissues, even from sparse annotation.

    DOI: 10.1109/CBS.2017.8266085

    Scopus

  95. Artificial Intelligence for Endocytoscopy Provides Fully Automated Diagnosis of Histological Remission in Ulcerativ E. Coli Tis Invited Reviewed

    Yasuharu Maeda, Kudo Shinei, Mori Yuichi, Misawa Masashi, Wakamura Kunihiko, Hayashi Seiko, Ogata Noriyuki, Takeda Kenichi, Kudo Toyoki, Hayashi Takemasa, Katagiri Atsushi, Ishida Fumio, Ohtsuka Kazuo, Oda Masahiro, Mori Kensaku

    GASTROINTESTINAL ENDOSCOPY   Vol. 85 ( 5 ) page: AB248 - AB248   2017.5

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  96. Computer-Aided Diagnosis Based on Endocytoscopy With Narrow-Band Imaging Allows Accurate Diagnosis of Diminutive Colorectal Lesions Invited Reviewed

    Misawa Masashi, Kudo Shinei, Mori Yuichi, Takeda Kenichi, Kataoka Shinichi, Nakamura Hiroki, Maeda Yasuharu, Ogawa Yushi, Yamauchi Akihiro, Igarashi Kenta, Hayashi Takemasa, Kudo Toyoki, Wakamura Kunihiko, Katagiri Atsushi, Baba Toshiyuki, Ishida Fumio, Oda Masahiro, Mori Kensaku

    GASTROINTESTINAL ENDOSCOPY   Vol. 85 ( 5 ) page: AB57 - AB57   2017.5

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  97. Can Artificial Intelligence Correctly Diagnose Sessile Serrated Adenomas/Polyps? Invited Reviewed

    Mori Yuichi, Kudo Shinei, Ogawa Yushi, Misawa Masashi, Takeda Kenichi, Kudo Toyoki, Wakamura Kunihiko, Hayashi Takemasa, Ichimasa Katsuro, Maeda Yasuharu, Toyoshima Naoya, Nakamura Hiroki, Katagiri Atsushi, Baba Toshiyuki, Ishida Fumio, Oda Masahiro, Mori Kensaku, Inoue Haruhiro

    GASTROINTESTINAL ENDOSCOPY   Vol. 85 ( 5 ) page: AB510 - AB510   2017.5

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  98. Diagnostic Ability of Automated Diagnosis System Using Endocytoscopy for Invasive Colorectal Cancer Invited Reviewed

    Takeda Kenichi, Kudo Shinei, Mori Yuichi, Kataoka Shinichi, Yasuharu Maeda, Ogawa Yushi, Nakamura Hiroki, Misawa Masashi, Kudo Toyoki, Wakamura Kunihiko, Hayashi Takemasa, Katagiri Atsushi, Baba Toshiyuki, Hidaka Eiji, Ishida Fumio, Inoue Haruhiro, Oda Masahiro, Mori Kensaku

    GASTROINTESTINAL ENDOSCOPY   Vol. 85 ( 5 ) page: AB408 - AB408   2017.5

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  99. Influence of using 3D images and 3D-printed objects on the formation of spatial mental models of experts and novices Invited Reviewed

    MAEHIGASHI Akihiro, MIWA Kazuhisa, ODA Masahiro, NAKAMURA Yoshihiko, MORI Kensaku, IGAMI Tsuyoshi

    JSAI Technical Report, SIG-ALST   Vol. 79 ( 0 ) page: 08   2017.3

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    Authorship:Corresponding author   Language:Japanese   Publishing type:Research paper (scientific journal)   Publisher:The Japanese Society for Artificial Intelligence  

    DOI: 10.11517/jsaialst.79.0_08

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  100. 3D FCN Feature Driven Regression Forest-Based Pancreas Localization and Segmentation Invited Reviewed

    Oda Masahiro, Shimizu Natsuki, Roth Holger R., Karasawa Ken'ichi, Kitasaka Takayuki, Misawa Kazunari, Fujiwara Michitaka, Rueckert Daniel, Mori Kensaku

    DEEP LEARNING IN MEDICAL IMAGE ANALYSIS AND MULTIMODAL LEARNING FOR CLINICAL DECISION SUPPORT   Vol. 10553   page: 222 - 230   2017

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    This paper presents a fully automated atlas-based pancreas segmentation method from CT volumes utilizing 3D fully convolutional network (FCN) feature-based pancreas localization. Segmentation of the pancreas is difficult because it has larger inter-patient spatial variations than other organs. Previous pancreas segmentation methods failed to deal with such variations. We propose a fully automated pancreas segmentation method that contains novel localization and segmentation. Since the pancreas neighbors many other organs, its position and size are strongly related to the positions of the surrounding organs. We estimate the position and the size of the pancreas (localization) from global features by regression forests. As global features, we use intensity differences and 3D FCN deep learned features, which include automatically extracted essential features for segmentation. We chose 3D FCN features from a trained 3D U-Net, which is trained to perform multi-organ segmentation. The global features include both the pancreas and surrounding organ information. After localization, a patient-specific probabilistic atlas-based pancreas segmentation is performed. In evaluation results with 146 CT volumes, we achieved 60.6% of the Jaccard index and 73.9% of the Dice overlap.

    DOI: 10.1007/978-3-319-67558-9_26

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    Scopus

  101. 3Dプリンタ・ユーザーインターフェイス等の最新動向 Invited

    森 健策

    インナービジョン   Vol. 32   page: 44 - 45   2017

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  102. 3D Printing for Medical Application Invited

    MORI Kensaku

    Medical Imaging and Information Sciences   Vol. 34 ( 1 ) page: 1 - 6   2017

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    <p>This paper briefly introduces 3D printing technology for medical applications. 3D printing technology is obtaining a lot of attentions from various fields including rapid prototyping and manufacturing, home use and medical applications. Medical applications of 3D printing are including:(a)diagnostic aid,(b)surgical aid,(c)medical education,(d)medical training, and(e)re-generative medicine. In this short summary, we will briefly explain the various mechanism of 3D printing and several printing techniques for reproducing organ models by 3D printing techniques. Also the recent topics in international conferences are introduced here.</p>

    DOI: 10.11318/mii.34.1

    CiNii Research

  103. 3Dプリンタの医療応用 Invited

    森 健策

    医用画像情報学会雑誌   Vol. 34   page: 1 - 6   2017

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  104. 3Dプリンタ-の基礎と医療応用 Invited

    森 健策

    月刊心臓   Vol. 49   page: 1104 - 1113   2017

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  105. Automatic Segmentation of Head Anatomical Structures from Sparsely-annotated Images Invited Reviewed

    Sugino Takaaki, Roth Holger R., Eshghi Mohammad, Oda Masahiro, Chung Min Suk, Mori Kensaku

    2017 IEEE INTERNATIONAL CONFERENCE ON CYBORG AND BIONIC SYSTEMS (CBS)     page: 145 - 149   2017

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  106. Computer-Aided Diagnosis of Mammographic Masses Using Geometric Verification-Based Image Retrieval Invited Reviewed

    Li Qingliang, Shi Weili, Yang Huamin, Zhang Huimao, Li Guoxin, Chen Tao, Mori Kensaku, Jiang Zhengang

    MEDICAL IMAGING 2017: COMPUTER-AIDED DIAGNOSIS   Vol. 10134   2017

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    DOI: 10.1117/12.2255799

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  107. Comparison of the Deep-Learning-Based Automated Segmentation Methods for the Head Sectioned Images of the Virtual Korean Human Project Invited Reviewed

    Eshghi Mohammad, Roth Holger R., Oda Masahiro, Chung Min Suk, Mori Kensaku

    PROCEEDINGS OF THE FIFTEENTH IAPR INTERNATIONAL CONFERENCE ON MACHINE VISION APPLICATIONS - MVA2017     page: 290 - 293   2017

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  108. A study on improvement of airway segmentation using Hybrid method Invited Reviewed

    Qier M., Kitasaka T., Nimura Y., Oda M., Mori K.

    Proceedings - 3rd IAPR Asian Conference on Pattern Recognition, ACPR 2015     page: 549 - 553   2016.6

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    This paper presents a method for extracting an airway region from 3D chest CT volumes that uses a combination of tube enhancement filters, voxel classification based on machine learning methods and graph-cut algorithm. Lots of previous methods utilize region growing or level set algorithms without any prior knowledge of bronchi, which always fail when they reach to the peripheral bronchi. In this paper, a method of extraction based on airway shape and machine learning is proposed. The proposed method detects candidate voxels of bronchial regions by using two types of enhancement filters, and a classifier model is built for selecting the proper candidates regions based on intensity and shape features and finally the selected candidate voxels are connected by graph-cut algorithm. We applied this method on six cases of 3D chest CT volumes. The results show that this method can extract the smaller airway branches without leaking into the lung parenchyma areas.

    DOI: 10.1109/ACPR.2015.7486563

    Scopus

  109. Characterization of Colorectal Lesions Using a Computer-Aided Diagnostic System for Narrow-Band Imaging Endocytoscopy Invited Reviewed

    Misawa Masashi, Kudo Shin-ei, Mori Yuichi, Nakamura Hiroki, Kataoka Shinichi, Maeda Yasuharu, Kudo Toyoki, Hayashi Takemasa, Wakamura Kunihiko, Miyachi Hideyuki, Katagiri Atsushi, Baba Toshiyuki, Ishida Fumio, Inoue Haruhiro, Nimura Yukitaka, Mori Kensaku

    GASTROENTEROLOGY   Vol. 150 ( 7 ) page: 1531 - +   2016.6

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    DOI: 10.1053/j.gastro.2016.04.004

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  110. Influences of 3D images and 3D-printed objects on mental model construction of liver structure Invited Reviewed

    MAEHIGASHI Akihiro, MIWA Kazuhisa, ODA Masahiro, NAKAMURA Yoshihiko, MORI Kensaku, IGAMI Tsuyoshi

    JSAI Technical Report, SIG-ALST   Vol. 76 ( 0 ) page: 14   2016.3

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    Authorship:Corresponding author   Language:Japanese   Publishing type:Research paper (scientific journal)   Publisher:The Japanese Society for Artificial Intelligence  

    <p>In this study, we experimentally investigated the influence of a three-dimensional (3D) graphic image and a 3D-printed object on a spatial reasoning task in a situation where liver resection surgery was presupposed. The results of the study indicated that using a 3D-printed object produced more accurate and faster mental model construction of a liver structure than a 3D image. Using a 3D-printed object was assumed to reduce cognitive load and information accessing cost more than using a 3D image.</p>

    DOI: 10.11517/jsaialst.76.0_14

    CiNii Research

  111. Cascade registration of micro CT volumes taken in multiple resolutions Invited Reviewed

    Nagara K., Oda H., Nakamura S., Oda M., Homma H., Takabatake H., Mori M., Natori H., Rueckert D., Mori K.

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)   Vol. 9805 LNCS   page: 269 - 280   2016

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    In this paper, we present a preliminary report of a multiscale registration method between micro-focus X-ray CT (micro CT) volumes taken in different scales. 3D fine structures of target objects can be observed on micro CT volumes, which are difficult to observe on clinical CT volumes. Micro CT scanners can scan specimens in various resolutions. In their high resolution volumes, ultra fine structures of specimens can be observed, while scanned areas are limited to very small. On the other hand, in low resolution volumes, large areas can be captured, while fine structures of specimens are difficult to observe. The fusion volume of the high and low resolution volumes will have benefits of both. Because the difference of resolutions between the high and low resolution volumes may vary greatly, an intermediate resolution volume is required for successful fusion of volumes. To perform such volume fusion, a cascade multi-resolution registration technique is required. To register micro CT volumes that have quite different resolutions, we employ a cascade co-registration technique. In the cascade co-registration process, intermediate resolution volumes are used in a registration process of the high and low resolution volumes. In the registration between two volumes, we apply two steps registration techniques. In the first step, a block division is used to register two resolution volumes. Afterward, we estimate the fine spatial positions relating the registered two volumes using the Powell method. The registration result can be used to generate a fusion volume of the high and low resolution volumes.

    DOI: 10.1007/978-3-319-43775-0_24

    Scopus

  112. 3Dプリンタ・ユーザーインターフェイス等の最新動向 Invited

    森 健策

    インナービジョン   Vol. -   page: 44 - 45   2016

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  113. 3Dプリンティングのハンドリングのノウハウ Invited

    森 健策

    インナービジョン   Vol. 31   page: 20 - 24   2016

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  114. Automated anatomical labeling of abdominal arteries and hepatic portal system extracted from abdominal CT volumes Invited Reviewed

    Matsuzaki Tetsuro, Oda Masahiro, Kitasaka Takayuki, Hayashi Yuichiro, Misawa Kazunari, Mori Kensaku

    MEDICAL IMAGE ANALYSIS   Vol. 20 ( 1 ) page: 152 - 161   2015.2

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    DOI: 10.1016/j.media.2014.11.002

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  115. Automated torso organ segmentation from 3D CT images using conditional random field Invited Reviewed

    Yukitaka Nimura, Yuichiro Hayashi, Takayuki Kitasaka, Kazunari Misawa, and Kensaku Mori

    Proceedings of SPIE Medical Imaging 2016   Vol. 9785   2015

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    DOI: 10.1117/12.2214845

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  116. 3Dプリンティングの現状と将来展望:医用画像処理と3Dプリンタによる臓器実体モデル作成とその利用 Invited

    森 健策

    光技術コンタクト   Vol. 53   page: 20 - 27   2015

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  117. A study on improvement of airway segmentation using Hybrid method Invited Reviewed

    Qier Meng, Kitasaka Takayuki, Nimura Yukitaka, Oda Masahiro, Mori Kensaku

    PROCEEDINGS 3RD IAPR ASIAN CONFERENCE ON PATTERN RECOGNITION ACPR 2015     page: 549 - 553   2015

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  118. Connection method of separated luminal regions of intestine from CT volumes Invited Reviewed

    Oda Masahiro, Kitasaka Takayuki, Furukawa Kazuhiro, Watanabe Osamu, Ando Takafumi, Hirooka Yoshiki, Goto Hidemi, Mori Kensaku

    MEDICAL IMAGING 2015: COMPUTER-AIDED DIAGNOSIS   Vol. 9414   2015

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    DOI: 10.1117/12.2081977

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  119. Automated Torso Organ Segmentation from 3D CT Images using Structured Perceptron and Dual Decompostion Invited Reviewed

    Nimura Yukitaka, Hayashi Yuichiro, Kitasaka Takayuki, Mori Kensaku

    MEDICAL IMAGING 2015: COMPUTER-AIDED DIAGNOSIS   Vol. 9414   2015

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    DOI: 10.1117/12.2081774

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  120. Automated branching pattern report generation for laparoscopic surgery assistance Invited Reviewed

    Oda Masahiro, Matsuzaki Tetsuro, Hayashi Yuichiro, Kitasaka Takayuki, Misawa Kazunari, Mori Kensaku

    MEDICAL IMAGING 2015: COMPUTER-AIDED DIAGNOSIS   Vol. 9414   2015

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    DOI: 10.1117/12.2082488

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  121. Development and clinical application of surgical navigation system for laparoscopic hepatectomy Invited Reviewed

    Hayashi Yuichiro, Igami Tsuyoshi, Hirose Tomoaki, Nagino Masato, Mori Kensaku

    MEDICAL IMAGING 2015: IMAGE-GUIDED PROCEDURES, ROBOTIC INTERVENTIONS, AND MODELING   Vol. 9415   2015

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    DOI: 10.1117/12.2082690

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  122. Development of a new detection device using a glass clip emitting infrared fluorescence for laparoscopic surgery of gastric cancer Invited Reviewed

    Inada Shunko Albano, Fuchi Shingo, Mori Kensaku, Hasegawa Junichi, Misawa Kazunari, Nakanishi Hayao

    6TH INTERNATIONAL CONFERENCE ON OPTICAL, OPTOELECTRONIC AND PHOTONIC MATERIALS AND APPLICATIONS (ICOOPMA) 2014   Vol. 619   2015

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    DOI: 10.1088/1742-6596/619/1/012033

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  123. SGSR: style-subnets-assisted generative latent bank for large-factor super-resolution with registered medical image dataset. Invited Reviewed International journal

    Tong Zheng, Hirohisa Oda, Yuichiro Hayashi, Shota Nakamura, Masaki Mori, Hirotsugu Takabatake, Hiroshi Natori, Masahiro Oda, Kensaku Mori

    International journal of computer assisted radiology and surgery   Vol. 19 ( 3 ) page: 493 - 506   2024.3

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    PURPOSE: We propose a large-factor super-resolution (SR) method for performing SR on registered medical image datasets. Conventional SR approaches use low-resolution (LR) and high-resolution (HR) image pairs to train a deep convolutional neural network (DCN). However, LR-HR images in medical imaging are commonly acquired from different imaging devices, and acquiring LR-HR image pairs needs registration. Registered LR-HR images have registration errors inevitably. Using LR-HR images with registration error for training an SR DCN causes collapsed SR results. To address these challenges, we introduce a novel SR approach designed specifically for registered LR-HR medical images. METHODS: We propose style-subnets-assisted generative latent bank for large-factor super-resolution (SGSR) trained with registered medical image datasets. Pre-trained generative models named generative latent bank (GLB), which stores rich image priors, can be applied in SR to generate realistic and faithful images. We improve GLB by newly introducing style-subnets-assisted GLB (S-GLB). We also propose a novel inter-uncertainty loss to boost our method's performance. Introducing more spatial information by inputting adjacent slices further improved the results. RESULTS: SGSR outperforms state-of-the-art (SOTA) supervised SR methods qualitatively and quantitatively on multiple datasets. SGSR achieved higher reconstruction accuracy than recently supervised baselines by increasing peak signal-to-noise ratio from 32.628 to 34.206 dB. CONCLUSION: SGSR performs large-factor SR while given a registered LR-HR medical image dataset with registration error for training. SGSR's results have both realistic textures and accurate anatomical structures due to favorable quantitative and qualitative results. Experiments on multiple datasets demonstrated SGSR's superiority over other SOTA methods. SR medical images generated by SGSR are expected to improve the accuracy of pre-surgery diagnosis and reduce patient burden.

    DOI: 10.1007/s11548-023-03037-3

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    PubMed

  124. YOLOv7-RepFPN: Improving real-time performance of laparoscopic tool detection on embedded systems Invited Reviewed

    Yuzhang Liu, Yuichiro Hayashi, Masahiro Oda, Takayuki Kitasaka, Kensaku Mori

    Healthcare Technology Letters     2024

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    This study focuses on enhancing the inference speed of laparoscopic tool detection on embedded devices. Laparoscopy, a minimally invasive surgery technique, markedly reduces patient recovery times and postoperative complications. Real-time laparoscopic tool detection helps assisting laparoscopy by providing information for surgical navigation, and its implementation on embedded devices is gaining interest due to the portability, network independence and scalability of the devices. However, embedded devices often face computation resource limitations, potentially hindering inference speed. To mitigate this concern, the work introduces a two-fold modification to the YOLOv7 model: the feature channels and integrate RepBlock is halved, yielding the YOLOv7-RepFPN model. This configuration leads to a significant reduction in computational complexity. Additionally, the focal EIoU (efficient intersection of union) loss function is employed for bounding box regression. Experimental results on an embedded device demonstrate that for frame-by-frame laparoscopic tool detection, the proposed YOLOv7-RepFPN achieved an mAP of 88.2% (with IoU set to 0.5) on a custom dataset based on EndoVis17, and an inference speed of 62.9 FPS. Contrasting with the original YOLOv7, which garnered an 89.3% mAP and 41.8 FPS under identical conditions, the methodology enhances the speed by 21.1 FPS while maintaining detection accuracy. This emphasizes the effectiveness of the work.

    DOI: 10.1049/htl2.12072

    Scopus

  125. Towards better laparoscopic video segmentation: A class-wise contrastive learning approach with multi-scale feature extraction Invited Reviewed

    Luyang Zhang, Yuichiro Hayashi, Masahiro Oda, Kensaku Mori

    Healthcare Technology Letters     2024

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    The task of segmentation is integral to computer-aided surgery systems. Given the privacy concerns associated with medical data, collecting a large amount of annotated data for training is challenging. Unsupervised learning techniques, such as contrastive learning, have shown powerful capabilities in learning image-level representations from unlabelled data. This study leverages classification labels to enhance the accuracy of the segmentation model trained on limited annotated data. The method uses a multi-scale projection head to extract image features at various scales. The partitioning method for positive sample pairs is then improved to perform contrastive learning on the extracted features at each scale to effectively represent the differences between positive and negative samples in contrastive learning. Furthermore, the model is trained simultaneously with both segmentation labels and classification labels. This enables the model to extract features more effectively from each segmentation target class and further accelerates the convergence speed. The method was validated using the publicly available CholecSeg8k dataset for comprehensive abdominal cavity surgical segmentation. Compared to select existing methods, the proposed approach significantly enhances segmentation performance, even with a small labelled subset (1–10%) of the dataset, showcasing a superior intersection over union (IoU) score.

    DOI: 10.1049/htl2.12069

    Scopus

  126. Revisiting instrument segmentation: Learning from decentralized surgical sequences with various imperfect annotations Invited Reviewed

    Zhou Zheng, Yuichiro Hayashi, Masahiro Oda, Takayuki Kitasaka, Kensaku Mori

    Healthcare Technology Letters     2024

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    This paper focuses on a new and challenging problem related to instrument segmentation. This paper aims to learn a generalizable model from distributed datasets with various imperfect annotations. Collecting a large-scale dataset for centralized learning is usually impeded due to data silos and privacy issues. Besides, local clients, such as hospitals or medical institutes, may hold datasets with diverse and imperfect annotations. These datasets can include scarce annotations (many samples are unlabelled), noisy labels prone to errors, and scribble annotations with less precision. Federated learning (FL) has emerged as an attractive paradigm for developing global models with these locally distributed datasets. However, its potential in instrument segmentation has yet to be fully investigated. Moreover, the problem of learning from various imperfect annotations in an FL setup is rarely studied, even though it presents a more practical and beneficial scenario. This work rethinks instrument segmentation in such a setting and propose a practical FL framework for this issue. Notably, this approach surpassed centralized learning under various imperfect annotation settings. This method established a foundational benchmark, and future work can build upon it by considering each client owning various annotations and aligning closer with real-world complexities.

    DOI: 10.1049/htl2.12068

    Scopus

  127. Artificial intelligence in a prediction model for post-ERCP pancreatitis Reviewed

    Takahashi Hidekazu, Eizaburo Ohno, Taiki Furukawa, Kentaro Yamao, Takuya Ishikawa, Yasuyuki Mizutani, Tadashi Iida, Yoshimune Shiratori, Shintaro Oyama, Junji Koyama, Kensaku Mori, Yuichiro Hayashi, Masahiro Oda, Takahisa Suzuki, Hiroki Kawashima

    Digestive Endoscopy     2023.7

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    DOI: 10.1111/den.14622

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  128. DEVELOPMENT OF A MACHINE-LEARNING MODEL FOR PREDICTING POST-ERCP PANCREATITIS Invited Reviewed

    Takahashi Hidekazu, Eizaburo Ohno, Taiki Furukawa, Kentaro Yamao, Takuya Ishikawa, Yasuyuki Mizutani, Tadashi Iida, Yoshimune Shiratori, Shintaro Oyama, Junji Koyama, Kensaku Mori, Yuichiro Hayashi, Masahiro Oda, Takahisa Suzuki, Hiroki Kawashima

    Gastrointestinal Endoscopy   Vol. 97 ( 6 ) page: AB656 - AB656   2023.6

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    DOI: 10.1016/j.gie.2023.04.1087

  129. Development of panorama vision ring for thoracoscopy. Invited Reviewed International journal

    Takayuki Kitasaka, Shota Nakamura, Yuichiro Hayashi, Tsuyoshi Nakai, Yasuhiro Nakai, Kensaku Mori, Toyofumi Fengshi Chen-Yoshikawa

    International journal of computer assisted radiology and surgery   Vol. 18 ( 5 ) page: 945 - 952   2023.5

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    PURPOSE: Minimally invasive surgery (MIS) using a thoraco- or laparoscope is becoming a more common surgical technique. In MIS, a magnified view from a thoracoscope helps surgeons conduct precise operations. However, there is a risk of the visible area becoming narrow. To confirm that the operation field is safe, the surgeon will draw the thoracoscope back to check the marginal area of the target and insert it again many times during MIS. To reduce the surgeon's load, we aim to visualize the entire thoracic cavity using a newly developed device called "panorama vision ring" (PVR). METHOD: The PVR is used instead of a wound retractor or a trocar. It is a ring-type socket with one big hole for the thoracoscope and four small holes for tiny cameras placed around the big hole. The views from the tiny cameras are fused into one wider view that visualizes the entire thoracic cavity. A surgeon can proceed with an operation by checking what exists outside of the thoracoscopic view. Also, she/he can check whether or not bleeding has occurred from the image of the entire cavity. RESULTS: We evaluated the view-expansion ability of the PVR by using a three-dimensional full-scale thoracic model. The experimental results showed that the entire thoracic cavity could be visible in a panoramic view generated by the PVR. We also demonstrated pulmonary lobectomy in virtual MIS using the PVR. Surgeons could perform a pulmonary lobectomy while checking the entire cavity. CONCLUSION: We developed the PVR, which uses tiny auxiliary cameras to create a panoramic view of the entire thoracic cavity during MIS. We aim to make MIS safer for patients and more comfortable for surgeons through the development of the PVR.

    DOI: 10.1007/s11548-023-02859-5

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  130. CT像からの腸閉塞検出における中心線を用いた後処理手法

    陳 思睿, 小田 紘久, 安 芹, 林 雄一郎, 北坂 孝幸, 滝本 愛太朗, 檜 顕成, 内田 広夫, 鈴木 耕次郎, 小田 昌宏, 森 健策

    電子情報通信学会技術研究報告(MI)信学技報   Vol. 122 ( 417 ) page: 223 - 228   2023.3

  131. 医用画像とAI Reviewed

    カレントテラピー   Vol. 41 ( 39 ) page: 79 - 79   2023.3

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  132. Average Templateを使ったCT像からのCOVID-19異常陰影領域セグメンテーション手法

    柳 凱, 小田 昌宏, 鄭 通, 林 雄一郎, 大竹 義人, 橋本 正弘, 明石 敏昭, 青木 茂樹, 森 健策

    電子情報通信学会技術研究報告(MI) 信学技報   Vol. 122 ( 47 ) page: 40 - 45   2023.3

  133. 術前画像情報を用いた腹腔鏡映像からの血管位置予測の検討

    榎本 圭吾, 林 雄一郎, 北坂 孝幸, 小田 昌宏, 三澤 一成, 森 健策

    電子情報通信学会技術研究報告(MI) 信学技報   Vol. 122 ( 417 ) page: 63 - 68   2023.3

  134. グラフニューラルネットワークを用いた血管名自動命名における臓器特徴の有効性の調査

    出口 智也, 林 雄一郎, 北坂 孝幸, 小田 昌宏, 三澤 一成, 森 健策

    電子情報通信学会技術研究報告(MI), 信学技報   Vol. 122 ( 417 ) page: 105 - 110   2023.3

  135. Comparative study of the Small Intestine segmentation based on 2D and 3D U-Nets

    Qin An, Hirohisa Oda, Sirui Chen, Yuichiro Hayashi, Takayuki Kitasaka, Hiroo Uchida, Akinari Hinoki, Kojiro Suzuki, Aitaro Takimoto, Masahiro Oda, Kensaku Mori

      Vol. 122 ( 417 ) page: 46 - 51   2023.3

  136. L-former : a lightweight transformer for realistic medical image generation and its application to super-resolution Reviewed

    Tong Zheng, Hirohisa Oda, Yuichiro Hayashi,shota Nakamura, Masaki Mori, Hirotsugu Takabatake, Hiroshi Natori, Masahiro Oda, Kensaku Mori

    proc. SPIE     2023.2

  137. Priority attention network with Bayesian learning for fully automatic segmentation of substantia nigra from neuromelanin MRI Invited Reviewed

    Tao Hu, Hayato Itoh, Masahiro Oda, Shinji Saiki, Nobutaka Hattori, Koji Kamagata, Shigeki Aoki, Kensaku Mori

    Proc.SPIE12464     2023.2

  138. `Thrombosis region extraction and quantitative analysis in confocal laser scanning microscopic image sequence in in-vivo imaging Invited Reviewed

    Yunheng Wu, Masahiro Oda,Yuichiro Hayashi, Shuntaro Kawamura, Takanori Takebe, Kensaku Mori

    Proc.SPIE     2023.2

  139. Real bronchoscopic images-based bronchial nomenclature: a preliminary study Invited Reviewed

    Cheng Wang, Yuichiro Hayashi, Masahiro Oda, Takayuki Kitasaka, Hirotsugu Takabatake, Masaki Mori, Hiroshi Natori, Kensaku Mori

    Proc.SPIE     2023.2

  140. Improved method for COVID-19 classification of complex-architecture CNN from chest CT volumes using orthogonal ensemble networks Invited Reviewed

    Ryo Toda, Masahiro Oda, Yuichiro Hayashi, Yoshito Otake, Masahiro Hashimoto, Toshiaki Akashi, Shigeki Aoki, Kensaku Mori

    Proc.SPIE     2023.2

  141. Octree cube constraints in PBD method for high resolution surgical simulation Reviewed

    Rintaro Miyazaki, Yuichiro Hayashi, Masahiro Oda, Kensaku Mori

    Proc.SPIE     2023.2

  142. Classification of COVID-19 cases from chest CT volumes using hybrid model of 3D CNN and 3D MLP-mixer Invited Reviewed

    Masahiro Oda, Tong Zheng, Yuichiro Hayashi, Yoshito Otake, Masahiro Hashimoto, Toshiaki Akashi, Shigeki Aoki, Kensaku Mori

    Proc.SPIE     2023.2

  143. A semantic segmentation method for laparoscopic images using semantically similar groups Reviewed

    Leo Uramoto, Yuichiro Hayashi, Masahiro Oda, Takayuki Kitasaka, Kazunari Misawa, Kensaku Mori

    Proc.SPIE     2023.2

  144. Oesophagus Achalasia Diagnosis from Esophagoscopy Based on a Serial Multi-scale Network Invited

    Kai Jiang, Masahiro Oda, Yuichiro Hayashi, Hironari Shiwaku, Masashi Misawa, Kensaku Mori

    Computer Methods in Biomechanics and Biomedical Engineering: Imaging &amp; Visualization   Vol. 11 ( 4 ) page: 1 - 10   2023.2

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    DOI: 10.1080/21681163.2022.2159534

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  145. KST-Mixer: kinematic spatio-temporal data mixer for colon shape estimation Invited

    Masahiro Oda, Kazuhiro Furukawa, Nassir Navab, Kensaku Mori

    Computer Methods in Biomechanics and Biomedical Engineering: Imaging &amp; Visualization   Vol. 11 ( 4 ) page: 1 - 7   2023.1

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    DOI: 10.1080/21681163.2022.2151938

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  146. 人工知能(AI)の最新動向 RSNA2022におけるAI関連セッション Reviewed

    インナービジョン   Vol. 38 ( 2 ) page: 33 - 34   2023.1

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  147. L-former : A Lightweight Transformer for Realistic Medical Image Generation and its Application to Super-resolution Invited Reviewed

    Tong Zheng, Hirohisa Oda, Yuichiro Hayashi, Shota Nakamura, Masaki Mori, Hirotsugu Takabatake, Hiroshi Natori, Masahiro Oda, Kensaku Mori

    MEDICAL IMAGING 2023   Vol. 12464   2023

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    Medical image analysis approaches such as data augmentation and domain adaption need huge amounts of realistic medical images. Generating realistic medical images by machine learning is a feasible approach. We propose L-former, a lightweight Transformer for realistic medical image generation. L-former can generate more reliable and realistic medical images than recent generative adversarial networks (GANs). Meanwhile, L-former does not consume as high computational cost as conventional Transformer-based generative models. L-former uses Transformers to generate low-resolution feature vectors at shallow layers, and uses convolutional neural networks to generate high-resolution realistic medical images at deep layers. Experimental results showed that L-former outperformed conventional GANs by FID scores 33.79 and 76.85 on two datasets, respectively. We further conducted a downstream study by using the images generated by L-former to perform a super-resolution task. A high PSNR score of 27.87 proved L-former's ability to generate reliable images for super-resolution and showed its potential for applications in medical diagnosis.

    DOI: 10.1117/12.2653776

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  148. Boundary-aware Feature and Prediction Refinement for Polyp Segmentation Reviewed

    ie Qiu, Yuichiro Hayashi, Masahiro Oda, Takayuki Kitasaka, Kensaku Mori, ``Boundary-aware Feature and Prediction Refinement for Polyp Segmentation

    Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization     2022.12

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  149. Classification and visual explanation for COVID-19 pneumonia from CT images using triple learning Invited Reviewed

    Sota Kato, Masahiro Oda, Kensaku Mori, Akinobu Shimizu, Yoshito Otake, Masahiro Hashimoto, Toshiaki Akashi, Kazuhiro Hotta

    Scientific Reports   Vol. 12 ( 1 ) page: 20840   2022.12

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    Abstract

    This study presents a novel framework for classifying and visualizing pneumonia induced by COVID-19 from CT images. Although many image classification methods using deep learning have been proposed, in the case of medical image fields, standard classification methods are unable to be used in some cases because the medical images that belong to the same category vary depending on the progression of the symptoms and the size of the inflamed area. In addition, it is essential that the models used be transparent and explainable, allowing health care providers to trust the models and avoid mistakes. In this study, we propose a classification method using contrastive learning and an attention mechanism. Contrastive learning is able to close the distance for images of the same category and generate a better feature space for classification. An attention mechanism is able to emphasize an important area in the image and visualize the location related to classification. Through experiments conducted on two-types of classification using a three-fold cross validation, we confirmed that the classification accuracy was significantly improved; in addition, a detailed visual explanation was achieved comparison with conventional methods.

    DOI: 10.1038/s41598-022-24936-6

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    Other Link: https://www.nature.com/articles/s41598-022-24936-6

  150. Positive-gradient-weighted object activation mapping: visual explanation of object detector towards precise colorectal-polyp localisation. Invited Reviewed International journal

    Hayato Itoh, Masashi Misawa, Yuichi Mori, Shin-Ei Kudo, Masahiro Oda, Kensaku Mori

    International journal of computer assisted radiology and surgery   Vol. 17 ( 11 ) page: 2051 - 2063   2022.11

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    PURPOSE: Precise polyp detection and localisation are essential for colonoscopy diagnosis. Statistical machine learning with a large-scale data set can contribute to the construction of a computer-aided diagnosis system for the prevention of overlooking and miss-localisation of a polyp in colonoscopy. We propose new visual explaining methods for a well-trained object detector, which achieves fast and accurate polyp detection with a bounding box towards a precise automated polyp localisation. METHOD: We refine gradient-weighted class activation mapping for more accurate highlighting of important patterns in processing a convolutional neural network. Extending the refined mapping into multiscaled processing, we define object activation mapping that highlights important object patterns in an image for a detection task. Finally, we define polyp activation mapping to achieve precise polyp localisation by integrating adaptive local thresholding into object activation mapping. We experimentally evaluate the proposed visual explaining methods with four publicly available databases. RESULTS: The refined mapping visualises important patterns in each convolutional layer more accurately than the original gradient-weighted class activation mapping. The object activation mapping clearly visualises important patterns in colonoscopic images for polyp detection. The polyp activation mapping localises the detected polyps in ETIS-Larib, CVC-Clinic and Kvasir-SEG database with mean Dice scores of 0.76, 0.72 and 0.72, respectively. CONCLUSIONS: We developed new visual explaining methods for a convolutional neural network by refining and extending gradient-weighted class activation mapping. Experimental results demonstrated the validity of the proposed methods by showing that accurate visualisation of important patterns and localisation of polyps in a colonoscopic image. The proposed visual explaining methods are useful for the interpreting and applying a trained polyp detector.

    DOI: 10.1007/s11548-022-02696-y

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  151. Pattern Analysis of Substantia Nigra in Parkinson Disease by Fifth-Order Tensor Decomposition and Multi-sequence MRI Reviewed

    Hayato Itoh, Tao Hu, Masahiro Oda, Shinji Saiki, Koji Kamagata, Nobutaka Hattori, Shigeki Aoki,Kensaku Mori

    LNCS13594     2022.10

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  152. Enhancing Model Generalization for Substantia Nigra Segmentation Using a Test-time Normalization-Based Method Reviewed

    Tao Hu, Hayato Itoh, Masahiro Oda, Yuichiro Hayashi, Zhongyang Lu, Shinji Saiki, Nobutaka Hattori, Koji Kamagata, Shigeki Aoki, Kanako K. Kumamaru, Toshiaki Akashi, Kensaku Mori

    LNCS13437     page: 736 - 744   2022.9

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  153. Geometric Constraints for Self-supervised Monocular Depth Estimation on Laparoscopic Images with Dual-task Consistency Reviewed

    Wenda Li, Yuichiro Hayashi, Masahiro Oda, akayuki Kitasaka, Kazunari Misawa, Kensaku Mori

    LNCS13434     page: 467 - 477   2022.9

  154. GNN による血管名自動命名手法における臓器特徴の利用に関する検討 Invited Reviewed

    出口 智也, 林 雄一郎, 北坂 孝幸, 小田 昌宏, 三澤 一成, 森 健策

     第41回日本医用画像工学会大会予稿集     2022.7

  155. Confocal Laser Scanning Microscope Image Super Resolution for Biomedical Research Based on Two-Stage Generative Adversarial Network Invited Reviewed

    Yunheng WU, Masahiro ODA, Yuichiro HAYASHI, Takanori TAKEBE, Shogo NAGATA, Shuntaro KAWAMURA, Kensaku MORI

        page: 138 - 139   2022.7

  156. Co-Training for Semi-Supervised CT Segmentation of COVID-19 Invited Reviewed

    Kai LIU, Masahiro ODA, Tong ZHENG, Yuichiro HAYASHI, Yoshito OTAKE, Masahiro HASHIMOTO, Toshiaki AKASHI, Shigeki AOKI, Kensaku MORI

        page: 114 - 115   2022.7

  157. A Novel Centroid-attention based Hybrid Model for Subarachnoid Hemorrhage Classification on Imbalanced Data Invited Reviewed

    Zhongyang LU, Masahiro ODA,1, Yuichiro HAYASHI, Tao Hu, Hayato ITOH, Takeyuki WATADANI, Osamu ABE,Kensaku MORI

        page: 104 - 105   2022.7

  158. テンソル分解を用いた黒質緻密部の3 次元パターン表現に関する初期的検討 Invited Reviewed

    伊東 隼人, 小田 昌宏, 斉木 臣二, 服部 信考, 鎌形 康司, 青木 茂樹, 森 健策

    第41回日本医用画像工学会大会予稿集     page: 124 - 125   2022.7

  159. 境界情報を考慮する損失関数を用いたFCN による腹部 CT 像からの臓器領域抽出に関する研究 Invited Reviewed

    大野 真奈, 申 忱, Holger R. Roth, 小田 昌宏, 林 雄一郎, 三澤 一成, 森 健策

    第41回日本医用画像工学会大会予稿集     page: 106 - 107   2022.7

  160. 大腸外科領域における情報支援内視鏡外科手術システムの開発 Invited Reviewed

    ]長谷川 寛, 北口 大地, 小島 成浩, 竹下 修由, 森 健策, 伊藤 雅昭

    日本コンピュータ外科学会誌 第31回日本コンピュータ外科学会大会特集号   Vol. 24 ( 2 )   2022.6

  161. 腹腔鏡映像からの血管領域自動抽出におけるDilated U‒Netの段数が 抽出精度に与える影響 Invited Reviewed

    榎本 圭吾 ,林 雄一郎,北坂 孝幸,小田 昌宏, 三澤 一成, 森 健策

    日本コンピュータ外科学会誌 第31回日本コンピュータ外科学会大会特集号   Vol. 24 ( 2 ) page: 22(5)-3   2022.6

  162. 腹腔鏡下胃切除術支援のための腹腔鏡映像からの膵臓領域抽出の検討 Invited Reviewed

    林 雄一郎, 辻 真治, 丘 杰, 小田 昌宏, 三澤 一成,森 健策

    日本コンピュータ外科学会誌 第31回日本コンピュータ外科学会大会特集号   Vol. 24 ( 2 ) page: 22(5)-4   2022.6

  163. nnU‒Netによる肺マイクロCT像からの小葉間隔壁抽出 Invited Reviewed

    深井 大輔,小田 紘久,椎名 健,林 雄一郎,鄭 通,中村 彰太, 小田 昌宏,森 健策

    日本コンピュータ外科学会誌 第31回日本コンピュータ外科学会大会特集号   Vol. 24 ( 2 ) page: 22(5)-6   2022.6

  164. コンピュータ外科におけるAIとVisionのドッキング―知能と知覚の結合による新たなコンピュータ外科 Invited Reviewed

    森 健策

    日本コンピュータ外科学会誌 第30回日本コンピュータ外科学会大会特集号   Vol. 24 ( 2 )   2022.6

  165. Laparoscopic image classification based on surgical areas in laparoscopic gastrectomy, Invited Reviewed

    Y. Hayashi, K. Misawa, K. Mori

    International Journal of Computer Assisted Radiology and Surgery   Vol. 17   page: s57 - 58   2022.6

  166. 3D bronchus anatomical structure measurement on real bronchoscopic images based on depth images estimated by deep neural network Reviewed

    C. Wang, Y. Hayashi, M. Oda, T. Kitasaka, H. Takabatake, M. Mori, H. Honma, K. Mori

    International Journal of Computer Assisted Radiology and Surgery   Vol. 17   page: s48 - s49   2022.6

  167. Extraction of respiratory bronchioles and alveolar ducts from micro-CT volumes with distance-based tubular structure filter Invited Reviewed

    T. Shiina, H. Oda, T. Zheng, S. Nakamura, Y. Hayashi, M. Oda, K. Mori

    International Journal of Computer Assisted Radiology and Surgery   Vol. 17   page: s117 - s118   2022.6

  168. 2D+3D registration in deformation-adaptive super-resolution for medical images Invited Reviewed

    T. Zheng, H. Oda, T. Hu, Y. Hayashi, S. Nakamura, M. Mori, H. Takabatake, H. Natori, M. Oda, K. Mori

    International Journal of Computer Assisted Radiology and Surgery   Vol. 17   page: s105 - s106   2022.6

  169. Automatic Detection of Bladder Tumors in Narrow-Band Imaging Cystoscopic Images by tiny-YOLO Invited Reviewed

    J. Mutaguchi, M. Oda, E. Kashiwagi, J. Inokuchi, K. Mori, M. Eto

    International Journal of Computer Assisted Radiology and Surgery   Vol. 17   page: s86 - s86   2022.6

  170. 30年間の医用画像研究経験を振り返り未来を考える Reviewed

    情報・システムソサイエティ誌   Vol. 27 ( 1 )   2022.5

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  171. Automated classification method of COVID-19 cases from chest CT volumes using 2D and 3D hybrid CNN for anisotropic volumes Reviewed

    Masahiro Oda, Tong Zheng, Yuichiro Hayashi, Yoshito Otake, Masahiro Hashimoto, Toshiaki Akashi, Shigeki Aoki, Kensaku Mori

    Proc. SPIE 12033     2022.3

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  172. Size-reweighted cascaded fully convolutional network for substantia nigra segmentation from T2 MRI Reviewed

    Tao Hu, Hayato Itoh, Masahiro Oda, Shinji Saiki, Nobutaka Hattori, Koji Kamagata, Shigeki Aoki, Kensaku Mori

    Proc. SPIE 12032     2022.3

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  173. Substantia nigra analysis by tensor decomposition of T2-weighted images for Parkinson’s disease diagnosis Invited Reviewed

    Hayato Itoh, Masahiro Oda, Shinji Saiki, Nobutaka Hattori, Koji Kamagata, Shigeki Aoki, Kensaku Mori

    Proc. SPIE 12032     2022.3

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  174. Self-supervised depth estimation with uncertainty-weight joint loss function based on laparoscopic videos Reviewed

    Wenda Li, Yuichiro Hayashi, Masahiro Oda, Takayuki Kitasaka, Kazunari Misawa, Kensaku Mori

    Proc. SPIE 12034     2022.3

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  175. Spatial label smoothing via aleatoric uncertainty for bleeding region segmentation from laparoscopic videos Reviewed

    Jie Qiu, Yuichiro Hayashi, Masahiro Oda, Takayuki Kitasaka, Nobuyoshi Takeshita, Masaaki Ito, Kensaku Mori

    Proc. SPIE 12032     2022.3

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  176. Effective hyperparameter optimization with proxy data for multi-organ segmentation Reviewed

    Chen Shen, Holger R. Roth, Vishwesh Nath, Yuichiro Hayashi, Masahiro Oda, Kazunari Misawa, Kensaku Mori

    Proc. SPIE 12032     2022.3

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  177. Coarse-to-fine cascade framework for cross-modality super-resolution on clinical/micro CT dataset, Reviewed

    Tong Zheng, Hirohisa Oda, Yuichiro Hayashi, Shota Nakamura, Masaki Mori, Hirotsugu Takabatake, Hiroshi Natori, Masahiro Oda, Kensaku Mori

    Proc. SPIE 12032     2022.3

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  178. Bronchial orifice tracking-based branch level estimation for bronchoscopic navigation Reviewed

    Cheng Wang, Yuichiro Hayashi, Masahiro Oda, Takayuki Kitasaka, Hitotsugu Takabatake, Masaki Mori, Hirotoshi Honma, Hiroshi Natori, Kensaku Mori

    Proc. SPIE 12034     2022.3

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  179. Taking full advantage of uncertainty estimation: an uncertainty-assisted two-stage pipeline for multi-organ segmentation Reviewed

    Zhou Zheng, Masahiro Oda, Kazunari Misawa, Kensaku Mori

    Proc. SPIE 12033     2022.3

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  180. Multiclass prediction for improving intestine segmentation on non-fecal-tagged CT volume Reviewed

    Hirohisa Oda, Yuichiro Hayashi, Takayuki Kitasaka, Aitaro Takimoto, Akinari,Hinoki, Hiroo Uchida, Kojiro Suzuki, Masahiro Oda, Kensaku Mori

    Proc. SPIE 12033     2022.3

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  181. 人工知能(AI)最新動向 学会発表を中心に(2)-Digital Poster発表を中心に

    森 健策

    インナービジョン   Vol. 37 ( 2 ) page: 30 - 31   2022.2

  182. Aorta-aware GAN for non-contrast to artery contrasted CT translation and its application to abdominal aortic aneurysm detection. Invited Reviewed International journal

    Tao Hu, Masahiro Oda, Yuichiro Hayashi, Zhongyang Lu, Kanako Kunishima Kumamaru, Toshiaki Akashi, Shigeki Aoki, Kensaku Mori

    International journal of computer assisted radiology and surgery   Vol. 17 ( 1 ) page: 97 - 105   2022.1

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    PURPOSE: Artery contrasted computed tomography (CT) enables accurate observations of the arteries and surrounding structures, thus being widely used for the diagnosis of diseases such as aneurysm. To avoid the complications caused by contrast agent, this paper proposes an aorta-aware deep learning method to synthesize artery contrasted CT volume form non-contrast CT volume. METHODS: By introducing auxiliary multi-resolution segmentation tasks in the generator, we force the proposed network to focus on the regions of aorta and the other vascular structures. Then, the segmentation results produced by the auxiliary tasks were used to extract aorta. The detection of abnormal CT images containing aneurysm was implemented by estimating the maximum axial radius of aorta. RESULTS: In comparison with the baseline models, the proposed network with auxiliary tasks achieved better performances with higher peak signal-noise ratio value. In aorta regions which are supposed to be the main region of interest in many clinic scenarios, the average improvement can be up to 0.33dB. Using the synthesized artery contrasted CT, the F score of aneurysm detection achieved 0.58 at slice level and 0.85 at case level. CONCLUSION: This study tries to address the problem of non-contrast to artery contrasted CT modality translation by employing a deep learning model with aorta awareness. The auxiliary tasks help the proposed model focus on aorta regions and synthesize results with clearer boundaries. Additionally, the synthesized artery contrasted CT shows potential in identifying slices with abdominal aortic aneurysm, and may provide an option for patients with contrast agent allergy.

    DOI: 10.1007/s11548-021-02492-0

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  183. 医用画像処理による人体構造の解析とその診断治療への応用 ~ 30年間の医用画像研究経験を振り返り未来を考える ~

    森 健策

    電子情報通信学会技術研究報告(MI), MI2021-74   Vol. 121 ( 347 ) page: 127 - 132   2022.1

  184. 胸部CT像からのCOVID-19に関連した所見文の自動生成の検討

    岡崎 真治, 林 雄一郎, 小田 昌宏, 橋本 正弘, 陣崎 雅弘, 明石 敏昭, 青木 茂樹, 森 健策

    電子情報通信学会技術研究報告(MI), MI2021-57   Vol. 121 ( 347 ) page: 49 - 54   2022.1

  185. 高精度な大腸ポリープ検出に向けた物体検出モデルの解析

    伊東 隼人, 三澤 将史, 森 悠一, 工藤 進英, 小田 昌宏, 森 健策

    電子情報通信学会技術研究報告(MI), MI2021-63   Vol. 121 ( 347 ) page: 86 - 87   2022.1

  186. 大規模腹腔鏡動画像データベース構築に向けたオンラインアノテーションツールの開発

    伊東 隼人, 潘 冬平, 小澤 卓也, 小田 昌宏, 竹下修由, 伊藤 雅昭, 森 健策

    電子情報通信学会技術研究報告(MI), MI2021-65   Vol. 121 ( 347 ) page: 86 - 87   2022.1

  187. 深層学習に基づくマウスのクラニアルウィンドウ画像における血管セグメンテーションの考察 Invited Reviewed

    呉 運恒, 小田 昌宏, 林 雄一郎, 武部 貴則, 森 健策

    電子情報通信学会技術研究報告(MI)   Vol. 121 ( 347 ) page: 174 - 179   2022.1

  188. Performance improvement of weakly supervised fully convolutional networks by skip connections for brain structure segmentation Reviewed

    Takaaki Sugino, Holger R. Roth, Mashiro Oda, Taichi Kin, Nobuhito Saito, Yoshikazu Nakajima, Kensaku Mori,

    Medical Physics   Vol. 48 ( 11 ) page: 7215 - 7227   2021.11

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    DOI: 10.1002/mp.15192

  189. 胸腔鏡下手術におけるパノラマビジョンリングの基礎開発 Reviewed

    北坂 孝幸,林 雄一郎,中村 彰太,芳川 豊史,森 健策,中井 剛,中井 康博

    日本コンピュータ外科学会誌 第30回日本コンピュータ外科学会大会特集号   Vol. 23 ( 4 ) page: 318   2021.11

  190. 腹腔鏡下胃切除術の手術ナビゲーションにおける位置合わせ誤差の補正に関する検討 Reviewed

    林 雄一郎, 三澤 一成,森 健策

    日本コンピュータ外科学会誌 第30回日本コンピュータ外科学会大会特集号   Vol. 23 ( 4 ) page: 301 - 302   2021.11

  191. 自己教師あり学習による腹腔鏡動画像の手術器具セグメンテーション Reviewed

    丘 傑,林 雄一郎,小澤 卓也,小田 昌宏, 北坂 孝幸,三澤 一成, 竹下 修由, 伊藤 雅昭, 森 健策

    日本コンピュータ外科学会誌 第30回日本コンピュータ外科学会大会特集号   Vol. 23 ( 4 ) page: 217 - 218   2021.11

  192. 距離変換と管状構造フィルタによる肺マイクロCT画像からの細気管支・肺胞管抽出手法の検討 Reviewed

    椎名 健, 小田 紘久, 鄭 通, 中村 彰太, 林 雄一郎, 小田 昌宏, 森 健策

    日本コンピュータ外科学会誌 第30回日本コンピュータ外科学会大会特集号   Vol. 23 ( 4 ) page: 219 - 220   2021.11

  193. 大規模腹腔鏡動画像データベース構築に向けたアノテーションツール開発 Reviewed

    伊東 隼人,潘 冬平,小澤 卓也,小田 昌宏, 竹下 修由, 伊藤 雅昭, 森 健策

    日本コンピュータ外科学会誌 第30回日本コンピュータ外科学会大会特集号   Vol. 23 ( 4 ) page: 243 - 244   2021.11

  194. 腹腔鏡下胃切除術支援のための腹腔鏡映像からの術中操作の予測に関する初期検討 Reviewed

    林 雄一郎, 三澤 一成, 森 健策

    日本コンピュータ外科学会誌 第30回日本コンピュータ外科学会大会特集号   Vol. 23 ( 4 ) page: 248   2021.11

  195. CT像の非等方性を考慮した3D CNNによるCOVID‒19症例の自動分類手法 Reviewed

    小田 昌宏, 鄭 通, 林 雄一郎, 大竹 義人, 橋本 正弘, 明石 敏明, 青木 茂樹, 森 健策

    日本コンピュータ外科学会誌 第30回日本コンピュータ外科学会大会特集号   Vol. 23 ( 4 ) page: 265 - 266   2021.11

  196. MRI 画像からの大脳基底核のAIセグメンテーション―Skip Connection による抽出精度向上の検討 Reviewed

    杉野貴明,金 太一,斎藤 季,川瀬 利弘,小野木 真哉,齊藤 延人,森 健策, 中島 義和

    日本コンピュータ外科学会誌 第30回日本コンピュータ外科学会大会特集号   Vol. 23 ( 4 ) page: 286   2021.11

  197. CT 像からの腸管領域抽出改善に関する基礎的検討 Reviewed

    小田紘久,林 雄一郎,北坂 孝幸,滝本 愛太朗,檜 顕成,内田 広夫,鈴木 耕次郎, 小田 昌宏, 森 健策

    日本コンピュータ外科学会誌 第30回日本コンピュータ外科学会大会特集号   Vol. 23 ( 4 ) page: 287 - 288   2021.11

  198. 複数の畳み込み範囲を持つグラフニューラルネットワークによる血管名自動命名手法の検討 Reviewed

    出口 智也,林 雄一郎,北坂 孝幸,小田 昌宏, 三澤 一成,森 健策

    日本コンピュータ外科学会誌 第30回日本コンピュータ外科学会大会特集号   Vol. 23 ( 4 ) page: 299 - 300   2021.11

  199. Binary polyp-size classification based on deep-learned spatial information Invited Reviewed

    Hayato Itoh, Masahiro Oda, Kai Jiang, Yuichi Mori, Masashi Misawa, Shin-Ei Kudo, Kenichiro Imai, Sayo Ito, Kinichi Hotta, Kensaku Mori

    International Journal of Computer Assisted Radiology and Surgery   Vol. 16 ( 10 ) page: 1817 - 1828   2021.10

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    Language:English   Publishing type:Research paper (scientific journal)   Publisher:Springer Science and Business Media LLC  

    DOI: 10.1007/s11548-021-02477-z

    DOI: 10.1007/s11548-021-02477-z

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    Other Link: https://link.springer.com/article/10.1007/s11548-021-02477-z/fulltext.html

  200. 胸部 CT 像からの COVID-19 症例の自動分類手法

    小田 昌宏, 鄭 通, 林 雄一郎, 大竹 義人, 橋本 正弘, 明石 敏昭, 森 健策

    第40回日本医用画像工学会大会予稿集     page: 65 - 67   2021.10

  201. VR Organ Puzzle: A Virtual Reality Application for the Education of Human Anatomy

    Siqi LI, Yuichiro HAYASHI,Michitaka FUJIWARA, Masahiro ODA, Kensaku MORI

        page: 489 - 491   2021.10

  202. Non-contrast to Artery Contrast CT Translation Via Representation-Aligned Generative Model

    Tao HU, Masahiro ODA, Yuichiro HAYASHI, Zhongyang LU, Toshiaki AKASHI, Shigeki AOKI, Kensaku MORI

        2021.10

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  203. Clinical CT Super-resolution Utilizing Registered Clinical – Micro CT Database

    Tong ZHENG, Hirohisa ODA, Yuichiro HAYASHI, Shota NAKAMURA, Masaki MORI, Hirotsugu TAKABATAKE, Hiroshi NATORI, Masahiro ODA, Kensaku MORI

        page: 394 - 400   2021.10

  204. 距離マップを利用した肺マイクロ CT 像からの肺胞抽出

    椎名 健, 小田 紘久, 鄭 通, 中村 彰太, 林 雄一郎, 小田 昌宏, 森 健策

    第40回日本医用画像工学会大会予稿集     page: 374 - 377   2021.10

  205. ピットパターン特徴量の解析に向けた超拡大内視鏡画像の再構成法に関する初期的検討

    伊東 隼人, 小田 昌宏, 森 悠一, 三澤 将史, 工藤 進英, 森 健策

    第40回日本医用画像工学会大会予稿集     page: 309 - 317   2021.10

  206. 深層学習とディジタルファントムを用いた骨陰影低減技術の開発

    五島 風汰, 田中 利恵, 小田 昌宏, 森 健策, 高田 宗尚, 田村 昌也, 松本 勲

    第40回日本医用画像工学会大会予稿集     page: 270 - 272   2021.10

  207. 3D Kidney Tumor Semantic Segmentation using Cascaded Convolutional Networks Invited Reviewed

    第40回日本医用画像工学会大会予稿集     page: 243 - 248   2021.10

  208. Attention 機構を導入したグラフニューラルネットワークによる,

    出口 智也, 林 雄一郎, 北坂 孝幸, 小田 昌宏,1 三澤 一成, 森 健策

    第40回日本医用画像工学会大会予稿集     page: 239 - 241   2021.10

  209. 深度情報を利用した FCN による腹腔鏡映像からの血管領域自動抽出の検討

    榎本 圭吾, 林 雄一郎, 北坂 孝幸, 小田 昌宏,1 伊藤 雅昭, 竹下 修由, 三澤 一成, 森 健策

    第40回日本医用画像工学会大会予稿集     page: 235 - 241   2021.10

  210. Vascular Structure Segmentation in Stereomicroscope Image

    Yunheng WU, Masahiro ODA, Yuichiro HAYASHI, Takanori TAKEBE, Kensaku MORI

        page: 229 - 234   2021.10

  211. Synthesized Perforation Detection from Endoscopy Videos Using Model Training with Synthesized Images by GAN

    Kai Jiang, Hayato Itoh, Masahiro Oda, Taishi Okumura, Yuichi Mori, Masashi Misawa, Takemasa Hayashi, Shin-Ei Kudo, Kensaku Mori

        page: 199 - 201   2021.10

  212. Improving Classification Accuracy of Hands' Bone Marrow Edema by Transfer Learning

    Dongping PAN, Masahiro ODA, Kou KATAYAMA, Takanobu Okubo, Kensaku MORI

        page: 150 - 157   2021.10

  213. 腸閉塞・イレウスの病変箇所特定における診断支援システムの精度評価

    小田 紘久, 林 雄一郎, 北坂 孝幸, 玉田 雄大, 滝本 愛太朗, 檜 顕成, 内田 広夫, 鈴木 耕次郎, 小田 昌宏, 森 健策

    第40回日本医用画像工学会大会予稿集     page: 129 - 131   2021.10

  214. Self-attention Class Balanced DenseNet_LSTM framework for Subarachnoid Hemorrhage CT image Classification on Extremely Imbalanced Brain CT Dataset

    第40回日本医用画像工学会大会予稿集     page: 69 - 75   2021.10

  215. VR Organ Puzzle: A Virtual Reality Application for the Education of Human Anatomy

    Siqi LI, Yuichiro HAYASHI,Michitaka FUJIWARA, Masahiro ODA, Kensaku MORI

        page: 493 - 498   2021.10

  216. Multi-task Federated Learning for Heterogeneous Pancreas Segmentation Invited Reviewed International coauthorship

    Chen Shen, Pochuan Wang, Holger R. Roth, Dong Yang, Daguang Xu, Masahiro Oda, Weichung Wang, Chiou-Shann Fuh, Po-Ting Chen, Kao-Lang Liu, Wei-Chih Liao, Kensaku Mori

    LNCS12969     page: 101 - 110   2021.9

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  217. Super-Resolution by Latent Space Exploration: Training with Poorly-Aligned Clinical and Micro CT Image Dataset Invited Reviewed

    Tong Zheng, Hirohisa Oda, Yuichiro Hayashi, Shota Nakamura, Masahiro Oda, Kensaku Mori

    LNCS12965     page: 24 - 33   2021.9

  218. Intestine segmentation with small computational cost for diagnosis assistance of ileus and intestinal obstruction Invited Reviewed

    Hirohisa Oda, Yuichiro Hayashi, Takayuki Kitasaka, Aitaro Takimoto, Akinari Hinoki, Hiroo Uchida, Kojiro Suzuki, Masahiro Oda, Kensaku Mori

    LNCS 12969     page: 3 - 12   2021.9

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  219. 泌尿器科領域における画像処理

    森 健策

    泌尿器科   Vol. 14 ( 2 ) page: 213 - 220   2021.8

  220. Can artificial intelligence help to detect dysplasia in patients with ulcerative colitis? Invited International journal

    Yasuharu Maeda, Shin-Ei Kudo, Noriyuki Ogata, Masashi Misawa, Yuichi Mori, Kensaku Mori, Kazuo Ohtsuka

    Endoscopy   Vol. 53 ( 07 ) page: E273 - E274   2021.7

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    DOI: 10.1055/a-1261-2944

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  221. Micro-CT-assisted cross-modality super-resolution of clinical CT: utilization of synthesized training dataset Reviewed

    T. Zheng, H. Oda, S. Nakamura, M. Mori, H. Takabatake, H. Natori, M. Oda, K. Mori

    International Journal of Computer Assisted Radiology and Surgery   Vol. 16 ( sup.1 ) page: 12 - 14   2021.6

  222. Unsupervised colonoscopic depth estimation by domain translations with a Lambertian-reflection keeping auxiliary task Invited Reviewed

    Hayato Itoh, Masahiro Oda, Yuichi Mori, Masashi Misawa, Shin-Ei Kudo, Kenichiro Imai, Sayo Ito, Kinichi Hotta, Hirotsugu Takabatake, Masaki Mori, Hiroshi Natori, Kensaku Mori

    INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY   Vol. 16 ( 6 ) page: 989 - 1001   2021.6

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    Purpose A three-dimensional (3D) structure extraction technique viewed from a two-dimensional image is essential for the development of a computer-aided diagnosis (CAD) system for colonoscopy. However, a straightforward application of existing depth-estimation methods to colonoscopic images is impossible or inappropriate due to several limitations of colonoscopes. In particular, the absence of ground-truth depth for colonoscopic images hinders the application of supervised machine learning methods. To circumvent these difficulties, we developed an unsupervised and accurate depth-estimation method. Method We propose a novel unsupervised depth-estimation method by introducing a Lambertian-reflection model as an auxiliary task to domain translation between real and virtual colonoscopic images. This auxiliary task contributes to accurate depth estimation by maintaining the Lambertian-reflection assumption. In our experiments, we qualitatively evaluate the proposed method by comparing it with state-of-the-art unsupervised methods. Furthermore, we present two quantitative evaluations of the proposed method using a measuring device, as well as a new 3D reconstruction technique and measured polyp sizes. Results Our proposed method achieved accurate depth estimation with an average estimation error of less than 1 mm for regions close to the colonoscope in both of two types of quantitative evaluations. Qualitative evaluation showed that the introduced auxiliary task reduces the effects of specular reflections and colon wall textures on depth estimation and our proposed method achieved smooth depth estimation without noise, thus validating the proposed method. Conclusions We developed an accurate depth-estimation method with a new type of unsupervised domain translation with the auxiliary task. This method is useful for analysis of colonoscopic images and for the development of a CAD system since it can extract accurate 3D information.

    DOI: 10.1007/s11548-021-02398-x

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  223. AIによる内視鏡外科手術支援と開発基盤としての手術動画データベース構築 Invited Reviewed

    竹下 修由, 森 健策, 伊藤 正昭

    消化器外科   Vol. 44 ( 7 ) page: 1159 - 1166   2021.6

  224. Three-dimensional surgical plan printing for assisting liver surgery, Reviewed

    Y. Hayashi, T. Igami, Y. Nakamura, K. Mori

    International Journal of Computer Assisted Radiology and Surgery   Vol. 16 ( sup.1 ) page: 104 - 106   2021.6

  225. COVID-19 lung infection and normal region segmentation from CT volumes using FCN with local and global spatial feature encoder Reviewed

    M. Oda, Y. Hayashi, Y. Otake, M. Hashimoto, T. Akashi, K. Mori

    International Journal of Computer Assisted Radiology and Surgery   Vol. 16 ( sup.1 ) page: 19 - 20   2021.6

  226. Experimental evaluation of loss functions in YOLO-v3 training for the perforation detection and localization in colonoscopic videos Reviewed

    K. Jiang, H. Itoh, M. Oda, T. Okumura, Y. Mori, M. Misawa, T. Hayashi, S. E. Kudo, K. Mori

    International Journal of Computer Assisted Radiology and Surgery   Vol. 16 ( sup.1 ) page: 74 - 75   2021.6

  227. Blood vessel regions segmentation from laparoscopic videos using fully convolutional networks with multi field of view input Reviewed

    K. Mori, S. Morimitsu, S. Yamamoto, T. Ozawa, T. Kitasaka, Y. Hayashi, M. Oda, M. Ito, N. Takeshita, K. Misawa

    International Journal of Computer Assisted Radiology and Surgery   Vol. 16 ( sup.1 ) page: 56 - 57   2021.6

  228. `Intestine segmentation combining Watershed transformation and machine learning-based distance map estimation Reviewed

    H. Oda, Y. Hayashi, T. Kitasaka, Y. Tamada, A. Takimoto, A. Hinoki, H. Uchida, K. Suzuki, H. Itoh, M. Oda, K. Mori

    International Journal of Computer Assisted Radiology and Surgery   Vol. 16 ( sup.1 ) page: 89 - 90   2021.6

  229. Real-time deformation simulation of hollow organs based on XPBD with small time steps and air mesh for surgical simulation Reviewed

    S. Li, Y. Hayashi, M. Oda, K. Misawa, K. Mori

    International Journal of Computer Assisted Radiology and Surgery   Vol. 16 ( sup.1 ) page: 21 - 25   2021.6

  230. Spatial Information Considered Module based on Attention Mechanism for Self-Supervised Depth Estimation from Laparoscopic Image Pairs Reviewed

    W. Li, Y. Hayashi, M. Oda, T. Kitasaka, K. Misawa, K. Mori

    nternational Journal of Computer Assisted Radiology and Surgery   Vol. 16 ( sup.1 ) page: 45 - 46   2021.6

  231. 機械学習によるCOVID-19症例CT画像の診断支援 Reviewed

    森 健策

    映像情報メディア学会誌   Vol. 75 ( 3 ) page: 326 - 329   2021.5

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  232. Development of a computer-aided detection system for colonoscopy and a publicly accessible large colonoscopy video database (with video). Invited Reviewed International journal

    Masashi Misawa, Shin-Ei Kudo, Yuichi Mori, Kinichi Hotta, Kazuo Ohtsuka, Takahisa Matsuda, Shoichi Saito, Toyoki Kudo, Toshiyuki Baba, Fumio Ishida, Hayato Itoh, Masahiro Oda, Kensaku Mori

    Gastrointestinal endoscopy   Vol. 93 ( 4 ) page: 960 - 967   2021.4

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    BACKGROUND AND AIMS: Artificial intelligence (AI)-assisted polyp detection systems for colonoscopic use are currently attracting attention because they may reduce the possibility of missed adenomas. However, few systems have the necessary regulatory approval for use in clinical practice. We aimed to develop an AI-assisted polyp detection system and to validate its performance using a large colonoscopy video database designed to be publicly accessible. METHODS: To develop the deep learning-based AI system, 56,668 independent colonoscopy images were obtained from 5 centers for use as training images. To validate the trained AI system, consecutive colonoscopy videos taken at a university hospital between October 2018 and January 2019 were searched to construct a database containing polyps with unbiased variance. All images were annotated by endoscopists according to the presence or absence of polyps and the polyps' locations with bounding boxes. RESULTS: A total of 1405 videos acquired during the study period were identified for the validation database, 797 of which contained at least 1 polyp. Of these, 100 videos containing 100 independent polyps and 13 videos negative for polyps were randomly extracted, resulting in 152,560 frames (49,799 positive frames and 102,761 negative frames) for the database. The AI showed 90.5% sensitivity and 93.7% specificity for frame-based analysis. The per-polyp sensitivities for all, diminutive, protruded, and flat polyps were 98.0%, 98.3%, 98.5%, and 97.0%, respectively. CONCLUSIONS: Our trained AI system was validated with a new large publicly accessible colonoscopy database and could identify colorectal lesions with high sensitivity and specificity. (Clinical trial registration number: UMIN 000037064.).

    DOI: 10.1016/j.gie.2020.07.060

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  233. Contrastive Learningを用いた肺野CT画像からCOVID-19の自動判定 Invited Reviewed

    加藤 聡太, 堀田 一弘, 小田 昌宏, 森 健策, 大竹 義人, 橋本 正弘, 明石 敏昭

    電子情報通信学会技術研究報告(MI)   Vol. 120 ( 432 ) page: 82 - 86   2021.3

  234. カスケードCNNによる腹腔鏡動画からの出血領域自動抽出 Invited Reviewed

    山本 翔太, 林 雄一郎, 盛満 慎太郎, 北坂 孝幸, 小田 昌宏, 竹下 修由, 伊藤 雅昭, 森 健策

    電子情報通信学会技術研究報告(MI)   Vol. 120 ( 431 ) page: 172 - 175   2021.3

  235. Spectral-based Convolutional Graph Neural Networksを用いた腹部動脈領域の血管名自動命名に関する研究 Invited Reviewed

    日比 裕太, 林 雄一郎, 北坂 孝幸, 伊東 隼人, 小田 昌宏, 三澤 一成, 森 健策

    電子情報通信学会技術研究報告(MI)   Vol. 120 ( 431 ) page: 176 - 181   2021.3

  236. Artificial Intelligence System to Determine Risk of T1 Colorectal Cancer Metastasis to Lymph Node. Invited Reviewed International journal

    Shin-Ei Kudo, Katsuro Ichimasa, Benjamin Villard, Yuichi Mori, Masashi Misawa, Shoichi Saito, Kinichi Hotta, Yutaka Saito, Takahisa Matsuda, Kazutaka Yamada, Toshifumi Mitani, Kazuo Ohtsuka, Akiko Chino, Daisuke Ide, Kenichiro Imai, Yoshihiro Kishida, Keiko Nakamura, Yasumitsu Saiki, Masafumi Tanaka, Shu Hoteya, Satoshi Yamashita, Yusuke Kinugasa, Masayoshi Fukuda, Toyoki Kudo, Hideyuki Miyachi, Fumio Ishida, Hayato Itoh, Masahiro Oda, Kensaku Mori

    Gastroenterology   Vol. 160 ( 4 ) page: 1075 - 1084.e2   2021.3

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    BACKGROUND & AIMS: In accordance with guidelines, most patients with T1 colorectal cancers (CRC) undergo surgical resection with lymph node dissection, despite the low incidence (∼10%) of metastasis to lymph nodes. To reduce unnecessary surgical resections, we used artificial intelligence to build a model to identify T1 colorectal tumors at risk for metastasis to lymph node and validated the model in a separate set of patients. METHODS: We collected data from 3134 patients with T1 CRC treated at 6 hospitals in Japan from April 1997 through September 2017 (training cohort). We developed a machine-learning artificial neural network (ANN) using data on patients' age and sex, as well as tumor size, location, morphology, lymphatic and vascular invasion, and histologic grade. We then conducted the external validation on the ANN model using independent 939 patients at another hospital during the same period (validation cohort). We calculated areas under the receiver operator characteristics curves (AUCs) for the ability of the model and US guidelines to identify patients with lymph node metastases. RESULTS: Lymph node metastases were found in 319 (10.2%) of 3134 patients in the training cohort and 79 (8.4%) of /939 patients in the validation cohort. In the validation cohort, the ANN model identified patients with lymph node metastases with an AUC of 0.83, whereas the guidelines identified patients with lymph node metastases with an AUC of 0.73 (P < .001). When the analysis was limited to patients with initial endoscopic resection (n = 517), the ANN model identified patients with lymph node metastases with an AUC of 0.84 and the guidelines identified these patients with an AUC of 0.77 (P = .005). CONCLUSIONS: The ANN model outperformed guidelines in identifying patients with T1 CRCs who had lymph node metastases. This model might be used to determine which patients require additional surgery after endoscopic resection of T1 CRCs. UMIN Clinical Trials Registry no: UMIN000038609.

    DOI: 10.1053/j.gastro.2020.09.027

    DOI: 10.1053/j.gastro.2020.09.027

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  237. Label cleaning and propagation for improved segmentation performance using fully convolutional networks. Invited Reviewed International journal

    Takaaki Sugino, Yutaro Suzuki, Taichi Kin, Nobuhito Saito, Shinya Onogi, Toshihiro Kawase, Kensaku Mori, Yoshikazu Nakajima

    International journal of computer assisted radiology and surgery   Vol. 16 ( 3 ) page: 349 - 361   2021.3

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    PURPOSE: In recent years, fully convolutional networks (FCNs) have been applied to various medical image segmentation tasks. However, it is difficult to generate a large amount of high-quality annotation data to train FCNs for medical image segmentation. Thus, it is desired to achieve high segmentation performances even from incomplete training data. We aim to evaluate performance of FCNs to clean noises and interpolate labels from noisy and sparsely given label images. METHODS: To evaluate the label cleaning and propagation performance of FCNs, we used 2D and 3D FCNs to perform volumetric brain segmentation from magnetic resonance image volumes, based on network training on incomplete training datasets from noisy and sparse annotation. RESULTS: The experimental results using pseudo-incomplete training data showed that both 2D and 3D FCNs could provide improved segmentation results from the incomplete training data, especially by using three orthogonal annotation images for network training. CONCLUSION: This paper presented a validation for label cleaning and propagation based on FCNs. FCNs might have the potential to achieve improved segmentation performances even from sparse annotation data including possible noises by manual annotation, which can be an important clue to more efficient annotation.

    DOI: 10.1007/s11548-021-02312-5

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  238. Unsupervised segmentation of COVID-19 infected lung clinical CT volumes using image inpainting and representation learning Reviewed

    Tong Zheng, Masahiro Oda, Chenglong Wang, Takayasu Moriya, Yuichiro Hayashi, Yoshito Otake, Masahiro Hashimoto, Toshiaki Akashi, Masaki Mori, Hirotsugu Takabatake, Hiroshi Natori, Kensaku Mori

    Proc. SPIE 11596, Medical Imaging, 2021: Image Processing     page: 115963F-1 - 6   2021.2

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    DOI: 10.1117/12.2580641

  239. Extremely imbalanced subarachnoid hemorrhage detection based on enseNet-LSTM network with class-balanced loss and transfer learning Reviewed

    Zhongyang Lu, Masahiro Oda, Yuichiro Hayashi, Tao Hu, Hayato Itoh, Takeyuki Watadani, Osamu Abe, Masahiro Hashimoto, Masahiro Jinzaki, Kensaku Mori

    Proceedings Volume 11597, Medical Imaging 2021: Computer-Aided Diagnosis     page: 115971Z   2021.2

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    DOI: 10.1117/12.2582088

  240. Single-shot three-dimensional reconstruction for colonoscopic image analysis Reviewed

    Hayato Itoh, Masahiro Oda, Yuichi Mori, Masashi Misawa, Shin-ei Kudo, Kinnichi Hotta, Hirotsugu Takabatake, Masaki Mori, Hiroshi Natori, Kensaku Mori

    Proc. SPIE 11598, Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling     page: 115980E-1 - 6   2021.2

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    DOI: 10.1117/12.2582660

  241. Intestinal region reconstruction of ileus cases from 3D CT images based on graphical representation and its visualization Reviewed

    Hirohisa Oda, Yuichiro Hayashi, Takayuki Kitasaka, Yudai Tamada, Aitaro Takimoto, Akinari Hinoki, Hiroo Uchida, Kojiro Suzuki, Hayato Itoh, Masahiro Oda, Kensaku Mori

    Proc.SPIE 11597, Medical Imaging 2021: Computer-Aided Diagnosis     2021.2

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    DOI: 10.1117/12.2581261

  242. Lung infection and normal region segmentation from CT volumes of COVID-19 cases Reviewed

    Masahiro Oda, Yuichiro Hayashi, Yoshito Otake, Masahiro Hashimoto, Toshiaki Akashi, Kensaku Mori

    Proc. SPIE 11597, Medical Imaging 2021: Computer-Aided Diagnosis     page: 115972X-1 - 6   2021.2

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    DOI: 10.1117/12.2582066

  243. Extraction of lung and lesion regions from COVID-19 CT volumes using 3D fully convolutional networks Reviewed

    Yuichiro Hayashi, Masahiro Oda, Chen Shen, Masahiro Hashimoto, Yoshito Otake, Toshiaki Akashi, Kensaku Mori

    Proc.SPIE 11597, Medical Imaging 2021: Computer-Aided Diagnosis     page: 115972A-1 - 6   2021.2

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    DOI: 10.1117/12.2581818

  244. 人工知能(AI)最新動向 AI研究から見たRSNA-AIの広がりを感じる大会 Invited

    森 健策

    インナービジョン   Vol. 36 ( 2 ) page: 25 - 26   2021.2

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  245. CT画像XAI技術で「新型コロナウィルス肺炎」を85%精度で識別

    小田 昌宏, 森 健策

    C-press   Vol. 120   page: 5 - 6   2021.2

  246. COVID-19診断支援AI開発における名古屋大学の取り組み

    小田 昌宏, 鄭 通, 林 雄一郎, 森 健策

      Vol. 39 ( 1 ) page: 13 - 19   2021.2

  247. New method for the assessment of perineural invasion from perihilar cholangiocarcinoma Reviewed

    Hiroshi Tanaka, Tsuyoshi Igami, Yoshie Shimoyama, Tomoki Ebata, Yukihiro Yokoyama, Kensaku Mori, Masato Nagino,

    Surgery Today   Vol. 51 ( 2 ) page: 136 - 143   2021.1

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    DOI: 10.1007/s00595-020-02071-x

  248. Current status and future perspective on artificial intelligence for lower endoscopy. Invited International journal

    Masashi Misawa, Shin-Ei Kudo, Yuichi Mori, Yasuharu Maeda, Yushi Ogawa, Katsuro Ichimasa, Toyoki Kudo, Kunihiko Wakamura, Takemasa Hayashi, Hideyuki Miyachi, Toshiyuki Baba, Fumio Ishida, Hayato Itoh, Masahiro Oda, Kensaku Mori

    Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society   Vol. 33 ( 2 ) page: 273 - 284   2021.1

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    The global incidence and mortality rate of colorectal cancer remains high. Colonoscopy is regarded as the gold standard examination for detecting and eradicating neoplastic lesions. However, there are some uncertainties in colonoscopy practice that are related to limitations in human performance. First, approximately one-fourth of colorectal neoplasms are missed on a single colonoscopy. Second, it is still difficult for non-experts to perform adequately regarding optical biopsy. Third, recording of some quality indicators (e.g. cecal intubation, bowel preparation, and withdrawal speed) which are related to adenoma detection rate, is sometimes incomplete. With recent improvements in machine learning techniques and advances in computer performance, artificial intelligence-assisted computer-aided diagnosis is being increasingly utilized by endoscopists. In particular, the emergence of deep-learning, data-driven machine learning techniques have made the development of computer-aided systems easier than that of conventional machine learning techniques, the former currently being considered the standard artificial intelligence engine of computer-aided diagnosis by colonoscopy. To date, computer-aided detection systems seem to have improved the rate of detection of neoplasms. Additionally, computer-aided characterization systems may have the potential to improve diagnostic accuracy in real-time clinical practice. Furthermore, some artificial intelligence-assisted systems that aim to improve the quality of colonoscopy have been reported. The implementation of computer-aided system clinical practice may provide additional benefits such as helping in educational poorly performing endoscopists and supporting real-time clinical decision-making. In this review, we have focused on computer-aided diagnosis during colonoscopy reported by gastroenterologists and discussed its status, limitations, and future prospects.

    DOI: 10.1111/den.13847

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  249. Dense-layer-based YOLO-v3 for detection and localization of colon perforations Invited Reviewed

    Kai Jiang, Hayato Itoh, Masahiro Oda, Taishi Okumura, Yuichi Mori, Masashi Misawa, Takemasa Hayashi, Shin-Ei Kudo, Kensaku Mori

    Medical Imaging 2021: Computer-Aided Diagnosis   Vol. 11597   page: 115971A-1 - 6   2021

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    DOI: 10.1117/12.2582300

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  250. Robust endocytoscopic image classification based on higher-order symmetric tensor analysis and multi-scale topological statistics. Invited Reviewed International journal

    Hayato Itoh, Yukitaka Nimura, Yuichi Mori, Masashi Misawa, Shin-Ei Kudo, Kinichi Hotta, Kazuo Ohtsuka, Shoichi Saito, Yutaka Saito, Hiroaki Ikematsu, Yuichiro Hayashi, Masahiro Oda, Kensaku Mori

    International journal of computer assisted radiology and surgery   Vol. 15 ( 12 ) page: 2049 - 2059   2020.12

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    PURPOSE: An endocytoscope is a new type of endoscope that enables users to perform conventional endoscopic observation and ultramagnified observation at the cell level. Although endocytoscopy is expected to improve the cost-effectiveness of colonoscopy, endocytoscopic image diagnosis requires much knowledge and high-level experience for physicians. To circumvent this difficulty, we developed a robust endocytoscopic (EC) image classification method for the construction of a computer-aided diagnosis (CAD) system, since real-time CAD can resolve accuracy issues and reduce interobserver variability. METHOD: We propose a novel feature extraction method by introducing higher-order symmetric tensor analysis to the computation of multi-scale topological statistics on an image, and we integrate this feature extraction with EC image classification. We experimentally evaluate the classification accuracy of our proposed method by comparing it with three deep learning methods. We conducted this comparison by using our large-scale multi-hospital dataset of about 55,000 images of over 3800 patients. RESULTS: Our proposed method achieved an average 90% classification accuracy for all the images in four hospitals even though the best deep learning method achieved 95% classification accuracy for images in only one hospital. In the case with a rejection option, the proposed method achieved expert-level accurate classification. These results demonstrate the robustness of our proposed method against pit pattern variations, including differences of colours, contrasts, shapes, and hospitals. CONCLUSIONS: We developed a robust EC image classification method with novel feature extraction. This method is useful for the construction of a practical CAD system, since it has sufficient generalisation ability.

    DOI: 10.1007/s11548-020-02255-3

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  251. [総論]AI時代を見据えた消化器外科手術 AIによる大腸T2癌リンパ節転移予測 Invited Reviewed

    中原 健太, 石田 文生, 一政 克朗, 森 悠一, 三澤 将史, 澤田 成彦, 工藤 進英, Villard Ben, 伊東 隼人, 森 健策

    日本消化器外科学会総会   Vol. 75回   page: WS15 - 6   2020.12

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  252. Improving contrast and spatial resolution in crystal analyzer based x ray dark field imaging Reviewed

    Masami Ando Yuki Nakao Ge Jin Hiroshi Sugiyama Naoki Sunaguchi Yongjin Sung Yoshifumi Suzuki Yong Sun Michio Tanimoto Katsuhiro Kawashima Tetsuya Yuasa Kensaku Mori Shu Ichihara Rajiv Gupta,

    Medical Physics   Vol. 47 ( 11 ) page: 5505 - 5513   2020.11

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    DOI: 10.1002/mp.14442

  253. 多元計算解剖モデルと人工知能に基づく診断治療支援 Invited Reviewed

    森 健策

    映像情報メディア学会誌   Vol. 74 ( 6 ) page: 909-915   2020.11

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  254. Preliminary study of Loss-Function Design for Detection and Localization of Perforations with YOLO-v3 in Colonoscopic Images

      Vol. 22 ( 4 ) page: 348 - 349   2020.11

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  255. 腸閉塞およびイレウスの診断支援システムにおける距離マップの導入

    小田 紘久, 林 雄一郎, 北坂 孝幸,玉田 雄大,滝本 愛太朗,檜 顕成, 内田 広夫,鈴木 耕次郎, 伊東 隼人,小田 昌宏,森 健策

    日本コンピュータ外科学会誌 第29回日本コンピュータ外科学会大会特集号   Vol. 22 ( 4 ) page: 282 - 284   2020.11

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  256. 局所情報に注目した腹腔鏡動画像からの出血領域抽出

    山本 翔太, 盛満 慎太郎,林 雄一郎,北坂 孝幸,小田 昌宏,伊藤 雅昭,竹下 修由, 森 健策

    日本コンピュータ外科学会誌 第29回日本コンピュータ外科学会大会特集号   Vol. 22 ( 4 ) page: 285 - 286   2020.11

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  257. Dilated convolution を用いた腹腔鏡動画像からの血管領域抽出における空間情報利用に関する検討

    盛満 慎太郎, 山本 翔太, 小澤 卓也, 北坂 孝幸, 林 雄一郎, 小田 昌宏, 伊藤 雅昭, 竹下 修由,三澤 一成, 森 健策

    日本コンピュータ外科学会誌 第29回日本コンピュータ外科学会大会特集号   Vol. 22 ( 4 ) page: 287 - 288   2020.11

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  258. 表現学習に基づくクラスタリングによるCOVID-19 肺CT像からの病変部抽出手法

    鄭 通, 小田 昌宏, 王 成龍,林 雄一郎,橋本 正弘, 大竹 義人,明石 敏昭, 森 健策

    日本コンピュータ外科学会誌 第29回日本コンピュータ外科学会大会特集号   Vol. 22 ( 4 ) page: 294 - 295   2020.11

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  259. 深層学習によるMRI画像からの神経鞘腫の自動位置検出

    小田 昌宏, 伊藤 定之, 今釜 史郎, 森 健策

    日本コンピュータ外科学会誌 第29回日本コンピュータ外科学会大会特集号   Vol. 22 ( 4 ) page: 296 - 297   2020.11

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  260. 腹腔鏡動画像用オンラインアノテーションツールの開発

    屠 芸豪, 伊東 隼人,小澤 卓也,小田 昌宏, 竹下 修由,伊藤 雅昭,森 健策

    日本コンピュータ外科学会誌 第29回日本コンピュータ外科学会大会特集号   Vol. 22 ( 4 ) page: 306 - 307   2020.11

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  261. 呼吸器外科における仮想胸腔鏡像による手術ナビゲーションシステムを用いた手術支援の検討

    林 雄一郎, 中村 彰太,森 健策

    日本コンピュータ外科学会誌 第29回日本コンピュータ外科学会大会特集号   Vol. 22 ( 4 ) page: 337   2020.11

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  262. 気管支鏡ナビゲーションのための敵対的生成による内視鏡画像深度推定の評価

    王 成, 小田 昌宏,林 雄一郎,北坂 孝幸,本間 裕敏,高畠 博嗣,森 雅樹,名取 博, 森 健策

    日本コンピュータ外科学会誌 第29回日本コンピュータ外科学会大会特集号   Vol. 22 ( 4 ) page: 338 - 339   2020.11

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  263. SUN database : 大腸ポリープ自動検出器の精度評価に向けた試験用画像

    伊東 隼人, 三澤 将史,森 悠一,小田 昌宏,工藤 進英, 森 健策

    日本コンピュータ外科学会誌 第29回日本コンピュータ外科学会大会特集号   Vol. 22 ( 4 ) page: 346 - 347   2020.11

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  264. Cost savings in colonoscopy with artificial intelligence-aided polyp diagnosis: an add-on analysis of a clinical trial (with video). Invited Reviewed International journal

    Yuichi Mori, Shin-Ei Kudo, James E East, Amit Rastogi, Michael Bretthauer, Masashi Misawa, Masau Sekiguchi, Takahisa Matsuda, Yutaka Saito, Hiroaki Ikematsu, Kinichi Hotta, Kazuo Ohtsuka, Toyoki Kudo, Kensaku Mori

    Gastrointestinal endoscopy   Vol. 92 ( 4 ) page: 905 - 911   2020.10

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    BACKGROUND AND AIMS: Artificial intelligence (AI) is being implemented in colonoscopy practice, but no study has investigated whether AI is cost saving. We aimed to quantify the cost reduction using AI as an aid in the optical diagnosis of colorectal polyps. METHODS: This study is an add-on analysis of a clinical trial that investigated the performance of AI for differentiating colorectal polyps (ie, neoplastic versus non-neoplastic). We included all patients with diminutive (≤5 mm) rectosigmoid polyps in the analyses. The average colonoscopy cost was compared for 2 scenarios: (1) a diagnose-and-leave strategy supported by the AI prediction (ie, diminutive rectosigmoid polyps were not removed when predicted as non-neoplastic), and (2) a resect-all-polyps strategy. Gross annual costs for colonoscopies were also calculated based on the number and reimbursement of colonoscopies conducted under public health insurances in 4 countries. RESULTS: Overall, 207 patients with 250 diminutive rectosigmoid polyps (104 neoplastic, 144 non-neoplastic, and 2 indeterminate) were included. AI correctly differentiated neoplastic polyps with 93.3% sensitivity, 95.2% specificity, and 95.2% negative predictive value. Thus, 105 polyps were removed and 145 were left under the diagnose-and-leave strategy, which was estimated to reduce the average colonoscopy cost and the gross annual reimbursement for colonoscopies by 18.9% and US$149.2 million in Japan, 6.9% and US$12.3 million in England, 7.6% and US$1.1 million in Norway, and 10.9% and US$85.2 million in the United States, respectively, compared with the resect-all-polyps strategy. CONCLUSIONS: The use of AI to enable the diagnose-and-leave strategy results in substantial cost reductions for colonoscopy.

    DOI: 10.1016/j.gie.2020.03.3759

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  265. Prediction of dose distribution from luminescence image of water using a deep convolutional neural network for particle therapy Reviewed

    Takuya Yabe, Seiichi Yamamoto, Masahiro Oda, Kensaku Mori, Toshiyuki Toshito, Takashi Akagi

    Medical Physics   Vol. 47 ( 9 ) page: 3882-3891   2020.9

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    DOI: 10.1002/mp.14372

  266. 名古屋大学スーパーコンピュータ「不老」における医用画像処理

    大島 聡史, 小田 昌宏, 片桐 孝洋, 森 健策

    電子情報通信学会技術研究報告(MI)   Vol. 120 ( 156 ) page: 69 - 74   2020.9

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  267. Preliminary Study of Perforation Detection and Localization for Colonoscopy Video

    Kai Jiang, Hayato Itoh, Masahiro Oda, Taishi Okumura, Yuichi Mori, Masashi Misawa, Takemasa Hayashi, Shin-Ei Kudo, Kensaku Mori

        page: 142 - 147   2020.9

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  268. 読影レポート解析を利用した医用画像データベースからのアノテーション付きデータセット作成に関する初期検討

    林 雄一郎, 鈴村 悠輝, 岡崎 真治, 小田 昌宏, 森 健策

    第39回日本医用画像工学会大会予稿集     page: 163 - 167   2020.9

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  269. A study on Subarachnoid Hemorrhage automatic detection utilized Transfer Learning on extremely imbalanced brain CT datasets

    Zhongyang Lu, Masahiro Oda, Yuichiro Hayashi, Tao Hu, Hayato Ito,Takeyuki Watadani,Osamu Abe,Masahiro Hashimoto,Masahiro Jinzaki,Kensaku Mori

        page: 168 - 172   2020.9

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  270. COVID-19 症例の定量評価のためのCT 像からの肺野自動セグメンテーション

    小田 昌宏, 林 雄一郎, 大竹 義人, 橋本 正弘, 明石 敏昭, 森 健策

    第39回日本医用画像工学会大会予稿集     page: 181 - 184   2020.9

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  271. Preliminary Study on Classification of Interstitial Cystitis Using Cystoscopy Images

    Tao Chu, Masahiro Oda, Akira Furuta, Tokunori Yamamoto, Kensaku Mori

        page: 186 - 191   2020.9

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  272. Dilated convolution を用いた FCN による腹腔鏡動画像からの血管領域抽出

    盛満 慎太郎, 山本 翔太, 北坂 孝幸, 林 雄一郎, 小田 昌宏, 竹下 修由, 伊藤 雅昭, 三澤 一成, 森 健策

    第39回日本医用画像工学会大会予稿集     page: 230 - 233   2020.9

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  273. 大域及び局所情報を用いた深層学習による出血領域自動セグメンテーション

    山本 翔太, 盛満 慎太郎, 林 雄一郎, 北坂 孝幸, 小田 昌宏, 伊藤 雅昭, 竹下 修由, 森 健策

    第39回日本医用画像工学会大会予稿集     page: 246 - 249   2020.9

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  274. 広範囲の隣接関係を考慮したグラフニューラルネットワークを用いた腹部動脈血管名自動命名の検討

    日比 裕太, 林 雄一郎, 北坂 孝幸, 伊東 隼人, 小田 昌宏, 三澤 一成, 森 健策

    第39回日本医用画像工学会大会予稿集     page: 268 - 271   2020.9

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  275. Cross-phase CT Image Registration Using Convolutional Neural Network

    Tao Hu, Masahiro Oda, Yuichiro Hayashi, Zhongyang Lu, Kanako Kunishishima Kumamaru, Shigeki Aoki, Kensaku Mori

        page: 276 - 280   2020.9

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  276. Unsupervised 3D Super-resolution of Clinical CT Volumes by Utilizing Multi-axis 2D Super-resolution

    第39回日本医用画像工学会大会予稿集     page: 377 - 384   2020.9

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  277. Preliminary Study on Classification of Hands' Bone Marrow Edema Using X-ray Images

    Dongping Pan, Masahiro Oda, Kou Katayama, Takanobu Okubo, Kensaku Mori

        page: 488 - 493   2020.9

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  278. 大腸内視鏡のための教師なし深度画像推定法における補助タスク検討

    伊東 隼人, 小田 昌宏, 森 悠一, 三澤 将史, 工藤 進英, 堀田 欣一, 高畠 博嗣, 森 雅樹, 名取 博, 森 健策

    第39回日本医用画像工学会大会予稿集     page: 563 - 568   2020.9

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  279. Spatial Information Considered Self-Supervised Depth Estimation Based on Image Pairs from Stereo Laparoscope

    Wenda Li, Yuichiro Hayashi, Masahiro Oda, Takayuki Kitasaka, Kazunari Misawa, Kensaku Mori

        page: 602 - 606   2020.9

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  280. 腹腔鏡手術動画像データベース構築に向けたリモートアノテーションツールのプロトタイプ開発

    屠 芸豪, 伊東 隼人, 小澤 卓也, 小田 昌宏, 竹下 修由, 伊藤 雅昭, 森 健策

    第39回日本医用画像工学会大会予稿集     page: 611 - 615   2020.9

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  281. ニューラルネットワークとSpherical K-meansを用いた胃壁マイクロCT像からの層構造および腫瘍抽出の検討

    御手洗 翠, 小田 紘久, 杉野 貴明, 守谷 享泰, 伊東 隼人, 小田 昌宏, 小宮山 琢真, 古川 和宏, 宮原 良二, 藤城 光弘, 森 雅樹, 高畠 博嗣, 名取 博, 森 健策

    電子情報通信学会技術研究報告(MI)   Vol. 120 ( 156 ) page: 1 - 6   2020.9

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  282. 泌尿器科画像診断に用いられるAI技術とその応用

    森 健策

    泌尿器外科   Vol. 33 ( 6 ) page: 557-561   2020.6

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  283. Detecting ganglion cells on virtual slide images: Macroscopic masking by superpixel Reviewed

    H. Oda, Y. Tamada, K. Nishio, T. Kitasaka, H. Amano, K. Chiba, A. Hinoki, H. Uchida, M. Oda, K. Mori

    International Journal of Computer Assisted Radiology and Surgery   Vol. 15 ( 1 ) page: S169 - S170   2020.6

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  284. An Application of Multi-organ Segmentation from Thick-slice Abdominal CT Volumes using Transfer Learning Reviewed

    C. Shen, M. Oda, H. Roth, H. Oda, Y. Hayashi, K. Misawa, K. Mori

    International Journal of Computer Assisted Radiology and Surgery   Vol. 15 ( 1 ) page: S17 - S18   2020.6

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  285. Virtual cleansing by unpaired image translation of intestines for detecting obstruction Reviewed

    K. Nishio, H. Oda, T. Kitasaka, Y. Tamada, H. Amano, A. Takimoto, K. Chiba, Y. Hayashi, H. Itoh, M. Oda, A. Hinoki, H. Uchida, K. Mori

    International Journal of Computer Assisted Radiology and Surgery   Vol. 15 ( 1 ) page: S21 - S22   2020.6

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  286. TinyLoss: loss function for tiny image difference evaluation and its application to unpaired non-contrast to contrast abdominal CT estimation Reviewed

    M. Oda, T. Hu, K. K. Kumamaru, T. Akashi, S. Aoki, K. Mori

    International Journal of Computer Assisted Radiology and Surgery   Vol. 15 ( 1 ) page: S25 - S26   2020.6

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  287. SR-CycleGAN V2: CycleGAN-based unsupervised superresolution with pixel-shuffling Reviewed

    T. Zheng, H. Oda, T. Moriya, T. Sugino, S. Nakamura, M. Oda, M. Mori, H. Takabatake, H. Natori, K. Mori

    International Journal of Computer Assisted Radiology and Surgery   Vol. 15 ( 1 ) page: S27 - S28   2020.6

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  288. Blood vessel segmentation from laparoscopic video using ConvLSTM U-Net

    S. Morimitsu, S. Yamamoto, T. Ozawa, T. Kitasaka, Y. Hayashi, M. Oda, M. Ito, N. Takeshita, K. Misawa, K. Mori

    International Journal of Computer Assisted Radiology and Surgery   Vol. 15 ( 1 ) page: S63 - S64   2020.6

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  289. Extraction of blood vessel regions in liver from CT volumes using fully convolutional networks for computer assisted liver surgery Reviewed

    Y. Hayashi, C. Shen, T. Igami, M. Nagino, K. Mori

    International Journal of Computer Assisted Radiology and Surgery   Vol. 15 ( 1 ) page: S152 - S153   2020.6

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  290. AIによるSSA/Pの超拡大内視鏡診断 Invited Reviewed

    小川 悠史, 工藤 進英, 森 悠一, 三澤 将史, 片岡 伸一, 前田 康晴, 一政 克朗, 石垣 智之, 工藤 豊樹, 若村 邦彦, 林 武雅, 馬場 俊之, 石田 文生, 伊東 隼人, 小田 昌宏, 森 健策

    日本大腸検査学会雑誌   Vol. 36 ( 2 ) page: 125 - 125   2020.5

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  291. バーチャル・リアリティ手術シュミレーター(VRS)の意義と今後の展望

    藤原 道隆, 林 雄一郎, 高見 秀樹, 田中 千恵, 森 健策, 小寺 泰弘

    臨床外科   Vol. 75 ( 4 ) page: 476 - 482   2020.4

  292. Cardiac fiber tracking on super high-resolution CT images: a comparative study. Invited International journal

    Hirohisa Oda, Holger R Roth, Takaaki Sugino, Naoki Sunaguchi, Noriko Usami, Masahiro Oda, Daisuke Shimao, Shu Ichihara, Tetsuya Yuasa, Masami Ando, Toshiaki Akita, Yuji Narita, Kensaku Mori

    Journal of medical imaging (Bellingham, Wash.)   Vol. 7 ( 2 ) page: 026001 - 026001   2020.3

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    Purpose: High-resolution cardiac imaging and fiber analysis methods are required to understand cardiac anatomy. Although refraction-contrast x-ray CT (RCT) has high soft tissue contrast, it cannot be commonly used because it requires a synchrotron system. Microfocus x-ray CT ( μ CT ) is another commercially available imaging modality. Approach: We evaluate the usefulness of μ CT for analyzing fibers by quantitatively and objectively comparing the results with RCT. To do so, we scanned a rabbit heart by both modalities with our original protocol of prepared materials and compared their image-based analysis results, including fiber orientation estimation and fiber tracking. Results: Fiber orientations estimated by two modalities were closely resembled under the correlation coefficient of 0.63. Tracked fibers from both modalities matched well the anatomical knowledge that fiber orientations are different inside and outside of the left ventricle. However, the μ CT volume caused incorrect tracking around the boundaries caused by stitching scanning. Conclusions: Our experimental results demonstrated that μ CT scanning can be used for cardiac fiber analysis, although further investigation is required in the differences of fiber analysis results on RCT and μ CT .

    DOI: 10.1117/1.JMI.7.2.026001

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  293. 3Dプリンティングの最新動向 Invited

    森 健策

    インナービジョン   Vol. 35 ( 2 ) page: 36-37   2020.2

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  294. Spatial information-embedded fully convolutional networks for multi-organ segmentation with improved data augmentation and instance normalization Reviewed

    Chen Shen, Chenglong Wang, Holger R. Roth, Masahiro Oda, Yuichiro Hayashi, Kazunari Misawa, Kensaku Mori

    Medical Imaging 2020: Image Processing   Vol. 11313   2020.2

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  295. Usefulness of fine-tuning for deep learning based multi-organ regions segmentation method from non-contrast CT volumes using small training dataset Reviewed

    Yuichiro Hayashi, Chen Shen, Holger R. Roth, Masahiro Oda, Kazunari Misawa, Masahiro Jinzaki, Masahiro Hashimoto, Kanako K. Kumamaru, Shigeki Aoki, Kensaku Mori

    Medical Imaging 2020: Image Processing   Vol. 11314   2020.2

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  296. Automated eye disease classification method from anterior eye image using anatomical structure focused image classification technique Reviewed

    Masahiro Oda, Naoyuki Maeda, Takefumi Yamaguchi, Hideki Fukuoka, Yuta Ueno, Kensaku Mori

    Medical Imaging 2020: Image Processing   Vol. 11314   2020.2

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  297. Improved visual SLAM for bronchoscope tracking and registration with pre-operative CT images

    Cheng Wang, Masahiro Oda, Yuichiro Hayashi, Takayuki Kitasaka, Hirotoshi Honma, Hirotsugu Takabatake, Masaki Mori, Hiroshi Natori, Kensaku Mori

    Medical Imaging 2020: Image Processing   Vol. 11315   2020.2

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  298. Visualising decision-reasoning regions in computer-aided pathological pattern diagnosis of endoscytoscopic images based on CNN weights analysis Reviewed

    Hayato Itoh, Zhongyang Lu, Yuichi Mori, Masashi Misawa, Masahiro Oda, Shin-ei Kudo, Kensaku Mori

    Medical Imaging 2020: Image Processing   Vol. 11314   2020.2

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  299. Organ segmentation from full-size CT images using memory-efficient FCN Reviewed

    Chenglong Wang, Masahiro Oda, Kensaku Mori

    Medical Imaging 2020: Image Processing   Vol. 11314   2020.2

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  300. Multi-modality super-resolution loss for GAN-based super-resolution of clinical CT images using micro CT image database Reviewed

    Tong Zheng, Hirohisa Oda, Takayasu Moriya, Takaaki Sugino, Shota Nakamura, Masahiro Oda, Masaki Mori, Hirotsugu Takabatake, Hiroshi Natori, Kensaku Mori

    Medical Imaging 2020: Image Processing   Vol. 11313   2020.2

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  301. Visualizing intestines for diagnostic assistance of ileus based on intestinal region segmentation from 3D CT images Reviewed

    Hirohisa Oda, Kohei Nishio, Takayuki Kitasaka, Hizuru Amano, Aitaro Takimoto, Akinari Hinoki, Hiroo Uchida, Kojiro Suzuki, Hayato Itoh, Masahiro Oda, Kensaku Mori

    Medical Imaging 2020: Image Processing   Vol. 11314   2020.2

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  302. 仮想腹腔鏡画像生成と深層学習による腹腔鏡画像からの術具領域セグメンテーション

    小澤 卓也, 林 雄一郎, 小田 紘久, 小田 昌宏, 北坂 孝幸, 竹下 修由, 伊藤 雅昭, 森 健策

    電子情報通信学会技術研究報告(MI)   Vol. 119 ( 399 ) page: 129 - 134   2020.1

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  303. 腹腔鏡下手術支援のためのU-Netに基づく腹腔鏡動画像からの出血領域の推定

    山本 翔太, 林 雄一郎, 盛満 慎太郎, 小澤 卓也, 北坂 孝幸, 小田 昌宏, 竹下 修由, 伊藤 雅昭, 森 健策

    電子情報通信学会技術研究報告(MI)   Vol. 119 ( 399 ) page: 209 - 214   2020.1

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  304. MICCAI 2019参加報告

    小田 昌宏, 伊東 隼人, 宮内 翔子, 諸岡 健一, 松崎 博貴, 花岡 昇平, 古川 亮, 増谷 佳孝, 森 健策

    電子情報通信学会技術研究報告(MI)   Vol. 119 ( 399 ) page: 219 - 226   2020.1

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  305. CycleGANによる腸管電子洗浄とその腸管閉塞部位検出への応用

    西尾 光平, 小田 紘久, 千馬 耕亮, 北坂 孝幸, 林 雄一郎, 伊東 隼人, 小田 昌宏, 檜 顕成, 内田 広夫, 森 健策

    電子情報通信学会技術研究報告(MI)   Vol. 119 ( 399 ) page: 243 - 248   2020.1

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  306. 臨床肺CT画像と切除肺マイクロCT画像の非剛体位置合わせ手法の検討

    波多腰 慎矢, 小田 紘久, 林 雄一郎, Holger R. Roth, 中村 彰太, 小田 昌宏, 森 雅樹, 高畠 博嗣, 名取 博, 森 健策

    電子情報通信学会技術研究報告(MI)   Vol. 119 ( 399 ) page: 249 - 254   2020.1

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  307. Stable polyp-scene classification via subsampling and residual learning from an imbalanced large dataset. International journal

    Hayato Itoh, Holger Roth, Masahiro Oda, Masashi Misawa, Yuichi Mori, Shin-Ei Kudo, Kensaku Mori

    Healthcare technology letters   Vol. 6 ( 6 ) page: 237 - 242   2019.12

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    This Letter presents a stable polyp-scene classification method with low false positive (FP) detection. Precise automated polyp detection during colonoscopies is essential for preventing colon-cancer deaths. There is, therefore, a demand for a computer-assisted diagnosis (CAD) system for colonoscopies to assist colonoscopists. A high-performance CAD system with spatiotemporal feature extraction via a three-dimensional convolutional neural network (3D CNN) with a limited dataset achieved about 80% detection accuracy in actual colonoscopic videos. Consequently, further improvement of a 3D CNN with larger training data is feasible. However, the ratio between polyp and non-polyp scenes is quite imbalanced in a large colonoscopic video dataset. This imbalance leads to unstable polyp detection. To circumvent this, the authors propose an efficient and balanced learning technique for deep residual learning. The authors' method randomly selects a subset of non-polyp scenes whose number is the same number of still images of polyp scenes at the beginning of each epoch of learning. Furthermore, they introduce post-processing for stable polyp-scene classification. This post-processing reduces the FPs that occur in the practical application of polyp-scene classification. They evaluate several residual networks with a large polyp-detection dataset consisting of 1027 colonoscopic videos. In the scene-level evaluation, their proposed method achieves stable polyp-scene classification with 0.86 sensitivity and 0.97 specificity.

    DOI: 10.1049/htl.2019.0079

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  308. Realistic Endoscopic Image Generation Method Using Virtual-to-real Image-domain Translation

    Masahiro Oda, Kiyohito Tanaka, Hirotsugu Takabatake, Masaki Mori, Hiroshi Natori, Kensaku Mori

    MICCAI 2019 Healthcare Technology Letters   Vol. 6 ( 6 ) page: 214 - 219   2019.12

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    DOI: 10.1049/htl.2019.0071

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  309. Realistic Endoscopic Image Generation Method Using Virtual-to-real Image-domain Translation

    Masahiro Oda, Kiyohito Tanaka, Hirotsugu Takabatake, Masaki Mori, Hiroshi Natori, Kensaku Mori

    Healthcare Technology Letters   Vol. 6 ( 6 ) page: 214-219   2019.12

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  310. Automatic Quantitative Analysis of Kidney Tumor Using 3D Fully Convolutional Network

    Chenglong Wang, Masahiro Oda, Yuichiro Hayashi, Naoto Sassa, Tokunori Yamamoto, Kensaku Mori

    RSNA2019     page: UR002-EB-X   2019.12

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  311. Technique for Improving Accuracy of Deep Learning-based Multi-Organ Segmentation from CT Volumes

    Chen Shen, Hirohisa Oda, MENG, Masahiro Oda, Holger R. Roth, Yuichiro Hayashi, Kensaku Mori, Takayuki Kitasaka, Kazunari Misawa

    RSNA2019     page: N021-EC-X   2019.12

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  312. Micro Lung Cancer Analysis Based on Micro CT Imaging Using Generative Adversarial Network

    Kensaku Mori, Takayasu Moriya, Hirohisa Oda, MENG , Midori Mitarai, Masahiro Oda, Shota Nakamura, Takaaki Sugino, Holger R. Roth

    RSNA2019     page: CH007-EC-X   2019.12

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  313. Generative Adversarial Networks Showcase: Their Mechanisms and Radiological Applications

    Masahiro Oda, Hirohisa Oda, Kanako K. Kumamaru, Shigeki Aoki, Hiroshi Natori, Kensaku Mori, Masaki Mori, Hirotsugu Takabatake

    RSNA2019     page: AI020-EB-X   2019.12

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  314. 大腸肛門病理学におけるAI利用の将来像

    森 健策

    日本大腸肛門病学学術集会 抄録号     page: A17   2019.10

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  315. 機械学習を用いた医療支援

    森 健策

    第84回日本泌尿器気学会東部総会, プログラム 抄録集     page: 123   2019.10

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  316. 胸部領域AIの歴史と今後-歴史的研究を振り返りながら今後を展望する-

    森 健策

    臨床画像   Vol. 35 ( 10 ) page: 1139-1149   2019.10

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  317. Spaciousness Filters for Non-contrast CT Volume Segmentation of the Intestine Region for Emergency Ileus Diagnosis

    Hirohisa Oda, Kohei Nishio, Takayuki Kitasaka, Benjamin Villard, Hizuru Amano, Kosuke Chiba, Akinari Hinoki, Hiroo Uchida, Kojiro Suzuki, Hayato Itoh, Masahiro Oda, Kensaku Mori

    MICCAI 2019   Vol. LNCS 11840   page: 104-114   2019.10

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  318. Unsupervised Segmentation of Micro-CT Images of Lung Cancer Specimen Using Deep Generative Models

    Takayasu Moriya, Hirohisa Oda, Midori Mitarai, Shota Nakamura, Holger R. Roth, Masahiro Oda, Kensaku Mori

    MICCAI 2019   Vol. LNCS 11769   page: 240-248   2019.10

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  319. Tubular Structure Segmentation Using Spatial Fully Connected Network With Radial Distance Loss for 3D Medical images

    Chenglong Wang, Yuichiro Hayashi, Masahiro Oda, Hayato Itoh, Takayuki Kitasaka, Alejandro Frangi, Kensaku Mori

    MICCAI 2019   Vol. LNCS 11769   page: 348-356   2019.10

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  320. Intelligent Image Synthesis to Attack a egmentation CNN Using Adversarial Learning

    Liang Chen, Paul Bentley, Kensaku Mori, Kazunari Misawa, Michitaka Fujiwara, Daniel Rueckert

    MICCAI 2019   Vol. LNCS 11827   page: 90-99   2019.10

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  321. 人工知能時代の医療を考える

    森 健策

    EAJ NEWS「AI×医療」特集号   ( 181 ) page: 6-8   2019.10

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  322. Precise estimation of renal vascular dominant regions using spatially aware fully convolutional networks, tensor-cut and Voronoi diagrams

    Chenglong Wang, Holger R. Roth, Takayuki Kitasaka, Masahiro Oda, Yuichiro Hayashi, Yasushi Yoshino, Tokunori Yamamoto, Naoto Sassa, Momokazu Goto, Kensaku Mori

    Computerized Medical Imaging and Graphics   Vol. 77 ( 10642 ) page: 1-13   2019.10

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    DOI: 10.1016/j.compmedimag.2019.101642

  323. Unsupervised Segmentation of Micro-CT Images of Lung Cancer Specimen Using Deep Generative Models

    Takayasu Moriya, Hirohisa Oda, Midori Mitarai, Shota Nakamura, Holger R. Roth, Masahiro Oda, Kensaku Mori,

    LNCS11769     page: 240-248   2019.10

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  324. Tubular Structure Segmentation Using Spatial Fully Connected Network With Radial Distance Loss for 3D Medical images

    Chenglong Wang, Yuichiro Hayashi, Masahiro Oda, Hayato Itoh, Takayuki Kitasaka, Alejandro Frangi, Kensaku Mori

    LNCS11769     page: 348-356   2019.10

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  325. Intelligent Image Synthesis to Attack a segmentation CNN Using Adversarial Learning

    Liang Chen, Paul Bentley, Kensaku Mori, Kazunari Misawa, Michitaka Fujiwara, Daniel Rueckert,

    LNCS11827     page: 90-99   2019.10

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  326. Spaciousness Filters for Non-contrast CT Volume Segmentation of the Intestine Region for Emergency Ileus Diagnosis

    Hirohisa Oda, Kohei Nishio, Takayuki Kitasaka, Benjamin Villard, Hizuru Amano, Kosuke Chiba, Akinari Hinoki, Hiroo Uchida, Kojiro Suzuki, Hayato Itoh, Masahiro Oda, Kensaku Mori

    LNCS 11840     page: 104-114   2019.10

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  327. Artificial Intelligence-assisted System Improves Endoscopic Identification of Colorectal Neoplasms. Reviewed International journal

    Shin-Ei Kudo, Masashi Misawa, Yuichi Mori, Kinichi Hotta, Kazuo Ohtsuka, Hiroaki Ikematsu, Yutaka Saito, Kenichi Takeda, Hiroki Nakamura, Katsuro Ichimasa, Tomoyuki Ishigaki, Naoya Toyoshima, Toyoki Kudo, Takemasa Hayashi, Kunihiko Wakamura, Toshiyuki Baba, Ishida Fumio, Haruhiro Inoue, Hayato Itoh, Masahiro Oda, Kensaku Mori

    Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association   Vol. 18 ( 8 ) page: 1874 - 1881   2019.9

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    BACKGROUND & AIMS: Precise optical diagnosis of colorectal polyps could improve the cost-effectiveness of colonoscopy and reduce polypectomy-related complications. However, it is difficult for community-based non-experts to obtain sufficient diagnostic performance. Artificial intelligence-based systems have been developed to analyze endoscopic images; they identify neoplasms with high accuracy and low interobserver variation. We performed a multi-center study to determine the diagnostic accuracy of EndoBRAIN, an artificial intelligence-based system that analyzes cell nuclei, crypt structure, and microvessels in endoscopic images, in identification of colon neoplasms. METHODS: The EndoBRAIN system was initially trained using 69,142 endocytoscopic images, taken at 520-fold magnification, from patients with colorectal polyps who underwent endoscopy at 5 academic centers in Japan from October 2017 through March 2018. We performed a retrospective comparative analysis of the diagnostic performance of EndoBRAIN vs that of 30 endoscopists (20 trainees and 10 experts); the endoscopists assessed images from 100 cases produced via white-light microscopy, endocytoscopy with methylene blue staining, and endocytoscopy with narrow-band imaging. EndoBRAIN was used to assess endocytoscopic, but not white-light, images. The primary outcome was the accuracy of EndoBrain in distinguishing neoplasms from non-neoplasms, compared with that of endoscopists, using findings from pathology analysis as the reference standard. RESULTS: In analysis of stained endocytoscopic images, EndoBRAIN identified colon lesions with 96.9% sensitivity (95% CI, 95.8%-97.8%), 100% specificity (95% CI, 99.6%-100%), 98% accuracy (95% CI, 97.3%-98.6%), a 100% positive-predictive value (95% CI, 99.8%-100%), and a 94.6% negative-predictive (95% CI, 92.7%-96.1%); these values were all significantly greater than those of the endoscopy trainees and experts. In analysis of narrow-band images, EndoBRAIN distinguished neoplastic from non-neoplastic lesions with 96.9% sensitivity (95% CI, 95.8-97.8), 94.3% specificity (95% CI, 92.3-95.9), 96.0% accuracy (95% CI, 95.1-96.8), a 96.9% positive-predictive value, (95% CI, 95.8-97.8), and a 94.3% negative-predictive value (95% CI, 92.3-95.9); these values were all significantly higher than those of the endoscopy trainees, sensitivity and negative-predictive value were significantly higher but the other values are comparable to those of the experts. CONCLUSIONS: EndoBRAIN accurately differentiated neoplastic from non-neoplastic lesions in stained endocytoscopic images and endocytoscopic narrow-band images, when pathology findings were used as the standard. This technology has been authorized for clinical use by the Japanese regulatory agency and should be used in endoscopic evaluation of small polyps more widespread clinical settings. UMIN clinical trial no: UMIN000028843.

    DOI: 10.1016/j.cgh.2019.09.009

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  328. 人工知能による画像診断支援-どこまでできたか.そして,その先は?

    森 健策

    第46回 日本小児内視鏡研究会 プログラム・抄録集     page: 11   2019.7

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  329. Artificial intelligence and upper gastrointestinal endoscopy: Current status and future perspective. International journal

    Yuichi Mori, Shin-Ei Kudo, Hussein E N Mohmed, Masashi Misawa, Noriyuki Ogata, Hayato Itoh, Masahiro Oda, Kensaku Mori

    Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society   Vol. 31 ( 4 ) page: 378 - 388   2019.7

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    With recent breakthroughs in artificial intelligence, computer-aided diagnosis (CAD) for upper gastrointestinal endoscopy is gaining increasing attention. Main research focuses in this field include automated identification of dysplasia in Barrett's esophagus and detection of early gastric cancers. By helping endoscopists avoid missing and mischaracterizing neoplastic change in both the esophagus and the stomach, these technologies potentially contribute to solving current limitations of gastroscopy. Currently, optical diagnosis of early-stage dysplasia related to Barrett's esophagus can be precisely achieved only by endoscopists proficient in advanced endoscopic imaging, and the false-negative rate for detecting gastric cancer is approximately 10%. Ideally, these novel technologies should work during real-time gastroscopy to provide on-site decision support for endoscopists regardless of their skill; however, previous studies of these topics remain ex vivo and experimental in design. Therefore, the feasibility, effectiveness, and safety of CAD for upper gastrointestinal endoscopy in clinical practice remain unknown, although a considerable number of pilot studies have been conducted by both engineers and medical doctors with excellent results. This review summarizes current publications relating to CAD for upper gastrointestinal endoscopy from the perspective of endoscopists and aims to indicate what is required for future research and implementation in clinical practice.

    DOI: 10.1111/den.13317

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  330. Artificial intelligence and upper gastrointestinal endoscopy: current status and future perspective Reviewed

    Yuichi Mori, Shinei Kudo, Hussein Ebaid Naeem Mohmed, Masashi Misawa, Noriyuki Ogata, Hayato Itoh, Masahiro Oda, Kensaku Mori

    Digestive endoscopy   Vol. 34 ( 4 ) page: 378-388   2019.7

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    DOI: 10.1111 den.13317

  331. 大腸内視鏡(コロノスコピー)画像診断支援ソフトウェアの開発

    森 健策

    インナービジョン 2019年7月号   Vol. 34 ( 7 ) page: 45   2019.7

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  332. 内視鏡検査手術における超音波画像の利用-マルチモダリティ画像統合-

    森 健策

    計測と制御   Vol. 58 ( 7 ) page: 541-544   2019.7

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  333. 単眼腹腔鏡映像からの奥行き推定を利用した術具セグメンテーション

    鈴木 拓矢, 道満 恵介, 目加田 慶人, 三澤 一成, 森 健策

    第38回日本医用画像工学会大会予稿集     page: OP1-11   2019.7

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  334. 多元計算解剖学のその先にあるもの

    森 健策

    第38回日本医用画像工学会大会予稿集     page: SY2-5   2019.7

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  335. 3D fully convolutional network を用いた腎腫瘍の定量評価における初期検討

    王 成龍, 小田 昌宏, 林 雄一郎, 佐々 直人, 山本 徳則, 森 健策

    第38回日本医用画像工学会大会予稿集     page: OP5-23   2019.7

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  336. 深層学習を用いた非造影 CT 画像からの複数臓器領域の抽出に関する検討

    林 雄一郎, 申 忱, Roth Holger, 小田 昌宏, 三澤 一成, 森 健策

    第38回日本医用画像工学会大会予稿集     page: OP5-14   2019.7

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  337. グラフ畳み込みニューラルネットワークを用いた腹部動脈血管名自動命名の初期検討

    日比 裕太, 林 雄一郎, 北坂 孝幸, 伊東 隼人, 小田 昌宏, 三澤 一成, 森 健策

    第38回日本医用画像工学会大会予稿集     page: OP5-11   2019.7

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  338. 移学習を用いた腹部 thick-slice CT 像における多臓器領域の自動抽出の初期検討

    申 忱, ロス ホルガー, 林 雄一郎, 小田 紘久, 小田 昌宏, 三澤 一成, 森 健策

    第38回日本医用画像工学会大会予稿集     page: OP4-15   2019.7

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  339. 深層学習を用いた腹腔鏡手術動画像の出血領域自動セグメンテーション

    山本 翔太, 小田 紘久, 林 雄一郎, 北坂 孝幸, 小田 昌宏, 伊藤 雅昭, 竹下 修由, 森 健策

    第38回日本医用画像工学会大会予稿     page: OP4-13   2019.7

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  340. 開腹手術映像における遮蔽物除去システムの VR 化

    北坂 孝幸, 伊藤 幹也, 駒形 和哉, 三澤 一成, 森 健策

    第38回日本医用画像工学会大会予稿集     page: OP4-10   2019.7

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  341. μ CT を用いた改良版 Cycle-GAN による臨床用 CT 像の超解像処理

    鄭 通, 小田 紘久, 守谷 享泰, 杉野 貴明, 中村 彰太, 小田 昌弘, 森 雅樹, 高畠 博嗣, 名取 博, 森 健策

    第38回日本医用画像工学会大会予稿集     page: OP4-02   2019.7

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  342. 小児腸閉塞患者の CT 像における CycleGAN を用いた電子洗浄手法の検討

    西尾 光平, 小田 紘久, 千馬 耕亮, 北坂 孝幸, 伊東 隼人, 小田 昌宏, 檜 顕成, 内田 広夫, 森 健策

    第38回日本医用画像工学会大会予稿集     page: OP3-20   2019.7

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  343. 腹腔鏡動画像からの Fully Convolutional Network による血管領域抽出

    盛満 慎太郎, 小澤 卓也, 北坂 孝幸, 林 雄一郎, 小田 昌宏, 伊藤 雅昭, 竹下 修由, 三澤 一成, 森 健策

    第38回日本医用画像工学会大会予稿集     page: OP3-12   2019.7

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  344. Generative Adversarial Frameworks を用いた腹部 CT 像における非造影像からの造影像の推定

    小田 昌宏, 隈丸 加奈子, 青木 茂樹, 森 健策

    第38回日本医用画像工学会大会予稿集     page: OP3-07   2019.7

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  345. AMED 大規模データベースを用いた CT 画像解析と病変検出への応用

    森 健策,小田 昌宏

    第38回日本医用画像工学会大会予稿集     page: SY1-5   2019.7

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  346. Polyp size classification in colorectal cancer using a Siamese network

        page: OP2-14   2019.7

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  347. 表現学習と SVM による胃壁マイクロ CT 像の半教師ありセグメンテーション手法

    御手洗 翠, 小田 紘久, 杉野 貴明, 守谷 享泰, 伊東 隼人, 小田 昌宏, 小宮山 琢真, 森 雅樹, 高畠 博嗣, 名取 博, 森 健策

    第38回日本医用画像工学会大会予稿集     page: OP2-08   2019.7

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  348. 深層学習における学習データセット規模拡大に応じた分類精度向上に関する実験的検討 ~超拡大大腸内視鏡画像における腫瘍性病変分類に向けた特徴量抽出~

    伊東 隼人, 森 悠一, 三澤 将史, 小田 昌宏, 工藤 進英, 森 健策

    第38回日本医用画像工学会大会予稿集     page: OP1-24   2019.7

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  349. 少量のラベルデータを用いた学習によるイレウス症例 CT 像における拡張腸管の自動抽出

    小田 紘久, 西尾 光平, 北坂 孝幸, 天野 日出, 千馬 耕亮, 内田 広夫, 鈴木 耕次郞, 伊東 隼人, 小田 昌宏, 森 健策

    第38回日本医用画像工学会大会予稿集     page: OP1-15   2019.7

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  350. 3D fully convolutional network を用いた腎腫瘍の定量評価における初期検討

    王 成龍, 小田 昌宏, 林 雄一郎, 佐々 直人, 山本 徳則, 森 健策

    第38回日本医用画像工学会大会予稿集     page: OP5-23   2019.7

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  351. AMED 大規模データベースを用いた CT 画像解析と病変検出への応用

    森 健策, 小田 昌宏

    第38回日本医用画像工学会大会予稿集     page: SY1-5   2019.7

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  352. Artificial intelligence and upper gastrointestinal endoscopy: current status and future perspective Reviewed

    Yuichi Mori, Shinei Kudo, Hussein Ebaid, Naeem Mohmed, Masashi Misawa, Noriyuki Ogata, Hayato Itoh, Masahiro Oda, Kensaku Mori

    Digestive endoscopy   Vol. 34 ( 4 ) page: 378-388   2019.7

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  353. Artificial intelligence and colonoscopy: Current status and future perspectives. Reviewed International journal

    Kudo SE, Mori Y, Misawa M, Takeda K, Kudo T, Itoh H, Oda M, Mori K

    Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society   Vol. 31 ( 4 ) page: 363 - 371   2019.7

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    BACKGROUND AND AIM: Application of artificial intelligence in medicine is now attracting substantial attention. In the field of gastrointestinal endoscopy, computer-aided diagnosis (CAD) for colonoscopy is the most investigated area, although it is still in the preclinical phase. Because colonoscopy is carried out by humans, it is inherently an imperfect procedure. CAD assistance is expected to improve its quality regarding automated polyp detection and characterization (i.e. predicting the polyp's pathology). It could help prevent endoscopists from missing polyps as well as provide a precise optical diagnosis for those detected. Ultimately, these functions that CAD provides could produce a higher adenoma detection rate and reduce the cost of polypectomy for hyperplastic polyps. METHODS AND RESULTS: Currently, research on automated polyp detection has been limited to experimental assessments using an algorithm based on ex vivo videos or static images. Performance for clinical use was reported to have >90% sensitivity with acceptable specificity. In contrast, research on automated polyp characterization seems to surpass that for polyp detection. Prospective studies of in vivo use of artificial intelligence technologies have been reported by several groups, some of which showed a >90% negative predictive value for differentiating diminutive (≤5 mm) rectosigmoid adenomas, which exceeded the threshold for optical biopsy. CONCLUSION: We introduce the potential of using CAD for colonoscopy and describe the most recent conditions for regulatory approval for artificial intelligence-assisted medical devices.

    DOI: 10.1111/den.13340

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  354. 医用画像AI

    森 健策

    医療機器学 第94回日本医療機器学会大会・学術集会   Vol. 89 ( 2 ) page: 107-108   2019.6

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  355. Automatic egistration of unordered point clouds for the study of abdominal organs and lymph node eformations

    B. Villard, K. Tachi, K. Misawa, M. Oda, K. Mori

    Computer Assisted Radiology 33rd International Congress and Exhibition CARS 2019     page: 0   2019.6

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  356. Semi-automated small intestine segmentation by fully convolutional networks and Hessian analysis

    K. Mori, H. Oda, T. Sugino, K. Nishio, K. Chiba, K. Oshima, T. Kitasaka, M. Oda, C. Shirota, A. Hinoki, H. Uchida

    International Journal of Computer Assisted Radiology and Surgery CARS 2019   Vol. 14 ( 1 ) page: s119-120   2019.6

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  357. 3D fully convolutional network-based head structure segmentation on multi-modal images from sparse annotation

    K. Mori, T. Sugino, H. Roth, M. Oda, T. Kin

    International Journal of Computer Assisted Radiology and Surgery CARS 2019   Vol. 14 ( 1 ) page: s120-121   2019.6

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  358. Non-contrast to contrasted abdominal CT volume regression using fully convolutional network

    M. Oda, K. K. Kumamaru, S. Aoki, K. Mori

    International Journal of Computer Assisted Radiology and Surgery CARS 2019   Vol. 14 ( 1 ) page: 103-104   2019.6

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  359. Computer-based virtual clinical trial for pulmonary function diagnosis with ynamic chest radiograph

    R. Tanaka, E. Samei, W. P. Segars, E. Abadi, H. Roth, H. Oda, K. Mori

    International Journal of Computer Assisted Radiology and Surgery CARS 2019   Vol. 14 ( 1 ) page: s20-21   2019.6

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  360. Optical coherence tomography classification of multiple retinal diseases using DenseNet

    C. Wang, M. Oda, Y. Itoh, K.Mori

    International Journal of Computer Assisted Radiology and Surgery CARS 2019   Vol. 14 ( 1 ) page: s69-70   2019.6

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  361. Artificial neural network for the prediction of colorectal lymph node metastasis

    B. Villard, H. Itoh, K. Ichimasa, Y. Mori, M. Misawa, M. Oda, S. Kudo, K. Mori

    International Journal of Computer Assisted Radiology and Surgery CARS 2019     page: 0   2019.6

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  362. Evaluation of squeeze and excitation fully convolutional networks for multi-organ segmentation

    C. Shen, F. Milletari, H. Roth, M. Oda, B. Villard, Y. Hayashi, K. Misawa, K. Mori

    International Journal of Computer Assisted Radiology and Surgery CARS 2019   Vol. 14 ( 1 ) page: s29-30   2019.6

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  363. Automatic segmentation of attention-aware artery region in laparoscopic colorectal surger

    S.Morimitsu, H. Itoh, T. Ozawa, H. Oda, T. Kitasaka, T. Sugino, Y. Hayashi, N. Takeshita, M. Ito, M. Oda, K. Mori

    International Journal of Computer Assisted Radiology and Surgery CARS 2019   Vol. 14 ( 1 ) page: s41-42   2019.6

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  364. Polyp-size determination method using short colonoscopic video clip information

    H. Itoh, Y. Mori, M. Misawa, M. Oda, S. E. Kudo, K. Mori

    International Journal of Computer Assisted Radiology and Surgery CARS 2019   Vol. 14 ( 1 ) page: s88-89   2019.6

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  365. Evaluation on econstruction accuracy of visual SLAM based bronchoscope tracking

    C. Wang, Masahiro Oda, Yuichiro Hayashi, Takayuki Kitasaka, Hayato Itoh, H. Honma, H. Takabatake, M. Mori, H. Natori, Kensaku Mori

    International Journal of Computer Assisted Radiology and Surgery CARS 2019   Vol. 14 ( 1 ) page: S24-25   2019.6

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  366. 3D fully convolutional network-based head structure segmentation on multi-modal images from sparse annotation

    K. Mori, T. Sugino, H. Roth, M. Oda, T. Kin

    International Journal of Computer Assisted Radiology and Surgery CARS 2019   Vol. 14 ( 1 ) page: s120-121   2019.6

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  367. Automatic egistration of unordered point clouds for the study of abdominal organs and lymph node eformations

    B. Villard, K. Tachi, K. Misawa, M. Oda, K. Mori

    Computer Assisted Radiology 33rd International Congress and Exhibition CARS 2019     page: 0   2019.6

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  368. Computer-based virtual clinical trial for pulmonary function diagnosis with ynamic chest radiograph

    R. Tanaka, E. Samei, W. P. Segars, E. Abadi, H. Roth, H. Oda, K. Mori

    International Journal of Computer Assisted Radiology and Surgery CARS 2019   Vol. 14 ( 1 ) page: s20-21   2019.6

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  369. Automatic segmentation of attention-aware artery region in laparoscopic colorectal surger

    S.Morimitsu, H. Itoh, T. Ozawa, H. Oda, T. Kitasaka, T. Sugino, Y. Hayashi, N. Takeshita, M. Ito, M. Oda, K. Mori

    International Journal of Computer Assisted Radiology and Surgery CARS 2019   Vol. 14 ( 1 ) page: s41-42   2019.6

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  370. Artificial neural network for the prediction of colorectal lymph node metastasis

    B. Villard, H. Itoh, K. Ichimasa, Y. Mori, M. Misawa, M. Oda, S. Kudo, K. Mori

    International Journal of Computer Assisted Radiology and Surgery CARS 2019     page: 0   2019.6

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  371. Polyp-size determination method using short colonoscopic video clip information

    H. Itoh, Y. Mori, M. Misawa, M. Oda, S. E. Kudo, K. Mori

    International Journal of Computer Assisted Radiology and Surgery CARS 2019   Vol. 14 ( 1 ) page: s88-89   2019.6

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  372. Optical coherence tomography classification of multiple retinal diseases using DenseNet

    C. Wang, M. Oda, Y. Itoh, K.Mori

    International Journal of Computer Assisted Radiology and Surgery CARS 2019   Vol. 14 ( 1 ) page: s69-70   2019.6

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    Language:English   Publishing type:Research paper (other academic)  

  373. Non-contrast to contrasted abdominal CT volume regression using fully convolutional network

    M. Oda, K. K. Kumamaru, S. Aoki, K. Mori

    International Journal of Computer Assisted Radiology and Surgery CARS 2019   Vol. 14 ( 1 ) page: 103-104   2019.6

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    Language:English   Publishing type:Research paper (other academic)  

  374. Evaluation on econstruction accuracy of visual SLAM based bronchoscope tracking

    C. Wang, Masahiro Oda, Yuichiro Hayashi, Takayuki Kitasaka, Hayato Itoh, H. Honma, H. Takabatake, M. Mori, H. Natori, Kensaku Mori

    International Journal of Computer Assisted Radiology and Surgery CARS 2019   Vol. 14 ( 1 ) page: S24-25   2019.6

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  375. Evaluation of squeeze and excitation fully convolutional networks for multi-organ segmentation

    C. Shen, F. Milletari, H. Roth, M. Oda, B. Villard, Y. Hayashi, K. Misawa, K. Mori

    International Journal of Computer Assisted Radiology and Surgery CARS 2019   Vol. 14 ( 1 ) page: s29-30   2019.6

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  376. Semi-automated small intestine segmentation by fully convolutional networks and Hessian analysis

    K. Mori, H. Oda, T. Sugino, K. Nishio, K. Chiba, K. Oshima, T. Kitasaka, M. Oda, C. Shirota, A. Hinoki, H. Uchida

    International Journal of Computer Assisted Radiology and Surgery CARS 2019   Vol. 14 ( 1 ) page: s119-120   2019.6

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  377. 深層学習を用いた脳CT像からの出血検出におけるデータ拡張とネットワーク構造の影響に関する考察

    魯 仲陽, 小田 昌宏, 鄭 通, 申 忱, 胡 涛, 渡谷 岳行, 阿部 修, 橋本 正弘, 陣崎 雅弘, 森 健策

    電子情報通信学会技術研究報告(MI)   Vol. 119 ( 51 ) page: 65-70   2019.5

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  378. ビッグデータとAIの医療応用

    森 健策

    最新醫學   Vol. 74 ( 3 ) page: 20-28   2019.3

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  379. Radiomics nomogram for predicting the malignant potential of gastrointestinal stromal tumours preoperatively Reviewed

    Tao Chen, Zhenyuan Ning, Lili Xu, Xingyu Feng, Shuai Han, Holger R. Roth, Wei Xiong, Xixi Zhao, Yanfeng Hu, Hao Liu, Jiang Yu, Yu Zhang, Yong Li, Yikai Xu, Kensaku Mori, Guoxin Li

    European radiology   Vol. 29 ( 3 ) page: 1074-1082   2019.3

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  380. Fully automated diagnostic system with artificial intelligence using endocytoscopy to identify the presence of histologic inflammation associated with ulcerative colitis (with video) Reviewed

    Yasuharu Maeda, Shin-eiKudo, Yuichi Mori, Masashi Misawa, Noriyuki Ogata, Seiko Sasanuma, Kunihiko Wakamura, Masahiro Oda, Kensaku Mori, Kazuo Ohtsuka

    Gastrointestinal endoscopy   Vol. 89 ( 2 ) page: 408-415   2019.2

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  381. 3Dプリンティングの最新動向

    森 健策

    インナービジョン 2019年2月号   Vol. 34 ( 2 ) page: 40-41   2019.2

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  382. Scanning, registration, and fiber estimation of rabbit hearts using micro-focus and refraction-contrast x-ray CT

    Hirohisa Oda, Holger R. Roth, Takaaki Sugino, Naoki Sunaguchi, Noriko Usami, Masahiro Oda, Daisuke Shimao, Shu Ichihara, Tetsuya Yuasa, Masami Ando, Toshiaki Akita, Yuji Narita, Kensaku Mori

    Proceedings of SPIE 10953, Medical Imaging 2019     page: 109531I-1-12   2019.2

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  383. Lung segmentation based on a deep learning approach for dynamic chest radiography

    Yuki Kitahara, Rie Tanaka, Holger Roth, Hirohisa Oda, Kensaku Mori, Kazuo Kasahara, Isao Matsumoto

    Proceedings of SPIE 10950, Medical Imaging 2019     page: 109503M-1-6   2019.2

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  384. Multiclass vertebral fracture classification using probability SVM with multi-feature selection

    Liyuan Zhang, Huamin Yang, Jiashi Zhao, Weili Shi, Yu Miao, Fei He, Wei He, Yanfang Li, Ke Zhang, Kensaku Mori, Zhengang Jiang

    Proceedings of SPIE 10950, Medical Imaging 2019     page: 1095025-1-11   2019.2

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  385. Spinal curvature segmentation and location by transfer learning

    Jiashi Zhao, Zhengang Jiang, Kensaku Mori, Liyuan Zhang, Wei He, Weili Shi, Yu Miao, Fei Yan, Fei He

    Proceedings of SPIE 10950, Medical Imaging 2019     page: 1095023-1-6   2019.2

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  386. Unsupervised segmentation of micro-CT images based on a hybrid of variational inference and adversarial learning

    Takayasu Moriya, Holger R. Roth, Shota Nakamura, Hirohisa Oda, Masahiro Oda, Kensaku Mori

    Proceedings of SPIE 10953, Medical Imaging 2019     page: 109530L-1-8   2019.2

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  387. Weakly-supervised deep learning of interstitial lung disease types on CT images

    Chenglong Wang, Takayasu Moriya, Yuichiro Hayashi, Holger Roth, Le Lu, Masahiro Oda, Hirotugu Ohkubo, Kennsaku Mori

    Proceedings of SPIE 10950, Medical Imaging 2019     page: 109501H-1-7   2019.2

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  388. Multi-class abdominal organs segmentation with improved V-Nets

    Chen Shen, Fausto Milletari, Holger R. Roth, Hirohisa Oda, Masahiro Oda, Yuichiro Hayashi, Kazunari Misawa, Kensaku Mori

    Proceedings of SPIE 10949, Medical Imaging 2019     page: 109490B-1-7   2019.2

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  389. Dynamic chest radiography for pulmonary function diagnosis: A validation study using 4D extended cardiac-torso (XCAT) phantom

    Rie Tanaka, Ehsan Samei, William Paul Segars, Ehsan Abadi, Holger Roth, Hirohisa Oda, Kensaku Mori

    Proceedings of SPIE 10948, Medical Imaging 2019     page: 109483I-1-7   2019.2

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  390. Polyp-size classification with RGB-D features for colonoscopy

    Hayato Itoh, Holger Roth, Yuichi Mori, Masashi Misawa, Masahiro Oda, Shin-Ei Kudo, Kensaku Mori

    Proceedings of SPIE 10950, Medical Imaging 2019     page: 1095015-1-7   2019.2

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  391. Colonoscope tracking method based on shape estimation network

    Masahiro Oda, Holger R. Roth, Takayuki Kitasaka, Kazuhiro Furukawa, Yoshiki Hirooka, Nassir Navab, Kensaku Mori

    Proceedings of SPIE 10951, Medical Imaging 2019     page: 109510Q-1-6   2019.2

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  392. Visual SLAM for bronchoscope tracking and bronchus reconstruction in bronchoscopic navigation

    Wang Cheng, Kensaku Mori, Masahiro Oda, Yuichiro Hayashi, Takayuki Kitasaka, Hirotoshi Honma, Hirotsugu Takabatake, Masaki Mori, Hiroshi Natori

    Proceedings of SPIE 10951, Medical Imaging 2019     page: 109510A-1-7   2019.2

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  393. 3Dプリンティングの最新動向

    森 健策

    インナービジョン 2019年2月号   Vol. 34 ( 2 ) page: 40-41   2019.2

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  394. Colonoscope tracking method based on shape estimation network

    Masahiro Oda, Holger R. Roth, Takayuki Kitasaka, Kazuhiro Furukawa, Yoshiki Hirooka, Nassir Navab, Kensaku Mori

    Proceedings of SPIE 10951, Medical Imaging 2019     page: 109510Q-1-6   2019.2

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  395. Wavelength Dependence of Ultrahigh-Resolution Optical Coherence Tomography Using Supercontinuum for Biomedical Imaging Reviewed

    Norihiko Nishizawa, Hiroyuki Kawagoe, Masahito Yamanaka, Miyoko atsushima, Kensaku Mori, Tsutomu Kawabe

    IEEE Journal of Selected Topics in Quantum Electronics   Vol. 25 ( 1 ) page: 7101115   2019.1

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    DOI: 10.1109/JSTQE.2018.2854595

  396. Investigation of extracting the interlobular septa with combination of Hessian analysis and radial structure tensor in micro-CT volume

    Xiaotian Zhao, Hirohisa Oda, Shota Nakamura, Yuichiro Hayashi, Hayato Itoh, Masahiro Oda, Kensaku Mori

    IFMIA 2019     page: 0   2019.1

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  397. AI支援手術に向けた鏡視下手術画像の自動認識システムの開発

    北口 大地, 松崎 博貴, 渡部 嘉気, 青柳 吉博, 佐藤 大介, 巣籠 悠輔, 原 聖吾, 森 健策, 伊藤 雅昭

    第1回日本メディカルAI学会学術集会, 日本メディカルAI学会誌   Vol. 1   page: 81   2019.1

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  398. 切除肺のマイクロCT像における3D-DBPNを用いた超解像の検討

    鄭 通, 小田 紘久, 小田 昌宏, 守谷 享泰, 中村 彰太, 森 健策

    第11回呼吸機能イメージング研究会学術集会, プログラム抄録集     page: 82   2019.1

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  399. MICCAI2018参加報告

    小田 昌宏, 大竹 義人, 伊東 隼人, 杉野 貴明, 斉藤 篤, 古川 亮, 大西 峻, 井宮 淳, 森 健策

    電子情報通信学会技術研究報告(MI)   Vol. 118 ( 412 ) page: 221-228   2019.1

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  400. 機械学習を用いた腹部動脈血管名自動命名におけるデータ拡張法の適用に関する検討

    鉄村 悠介, 林 雄一郎, 小田 昌宏, 北坂 孝幸, 三澤 一成, 森 健策

    電子情報通信学会技術研究報告(MI)   Vol. 118 ( 412 ) page: 191-196   2019.1

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  401. CTからの腹部多臓器抽出におけるgroup normalizationの影響に関する考察

    申 忱, Fausto Milletari, Holger R. Roth, 小田 紘久, 小田 昌宏, 林 雄一郎, 三澤 一成, 森 健策

    電子情報通信学会技術研究報告(MI)   Vol. 118 ( 412 ) page: 143-148   2019.1

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  402. 不均衡データからの特徴選択 超拡大内視鏡画像の病理類型分類に向けて

    伊東 隼人, 森 悠一, 三澤 将史, 小田 昌宏, 工藤 進英, 森 健策

    電子情報通信学会技術研究報告(MI)   Vol. 118 ( 412 ) page: 109-114   2019.1

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  403. 経時CT像間の腹部臓器の変形を考慮したリンパ節自動対応付け手法の検討

    舘 高基, 小田 昌宏, 林 雄一郎, 伊東 隼人, 中村 嘉彦, 北坂 孝幸, 三澤 一成, 森 健策

    電子情報通信学会技術研究報告(MI)   Vol. 118 ( 412 ) page: 97-102   2019.1

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  404. マルチモーダル画像を用いた深層学習ベースの頭部解剖構造抽出 少量画像データ学習における抽出精度検証

    杉野 貴明, Holger R. Roth, 小田 昌宏, 金 太一, 森 健策

    電子情報通信学会技術研究報告(MI)   Vol. 118 ( 412 ) page: 65-70   2019.1

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  405. 敵対的Dense U-netを用いた切除肺マイクロCT像の超解像

    鄭 通, 小田 紘久, Holger R. Roth, 小田 昌宏, 中村 彰太, 森 健策

    電子情報通信学会技術研究報告(MI)   Vol. 118 ( 412 ) page: 7-12   2019.1

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  406. Automated hand eye calibration in laparoscope holding robot for robot assisted surgery

    Shuai Jiang, Yuichiro Hayashi, Masahiro Oda, Takayuki Kitasaka, Kazunari Misawa, Kensaku Mori

    IFMIA 2019     page: 0   2019.1

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  407. CTからの腹部多臓器抽出におけるgroup normalizationの影響に関する考察

    申 忱, Fausto Milletari, Holger R. Roth, 小田 紘久, 小田 昌宏, 林 雄一郎, 三澤 一成, 森 健策

    電子情報通信学会技術研究報告(MI)   Vol. 118 ( 412 ) page: 143-148   2019.1

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  408. AI支援手術に向けた鏡視下手術画像の自動認識システムの開発

    北口 大地, 松崎 博貴, 渡部 嘉気, 青柳 吉博, 佐藤 大介, 巣籠 悠輔, 原 聖吾, 森 健策, 伊藤 雅昭

    第1回日本メディカルAI学会学術集会, 日本メディカルAI学会誌   Vol. 1   page: 81   2019.1

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  409. Automated hand eye calibration in laparoscope holding robot for robot assisted surgery

    Shuai Jiang, Yuichiro Hayashi, Masahiro Oda, Takayuki Kitasaka, Kazunari Misawa, Kensaku Mori

    IFMIA 2019     page: 0   2019.1

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  410. Regulatory Aspects on AI-based Medical Devices and Systems Reviewed

    CHINZEI Kiyoyuki, SHIMIZU Akinobu, MORI Kensaku, HARADA Kanako, TAKEDA Hideaki, HASHIZUME Makoto, ISHIZUKA Mayumi, KATO Nobumasa, KAWAMORI Ryuzo, KYO Shunei, NAGATA Kyosuke, YAMANE Takashi, SAKUMA Ichiro, OHE Kazuhiko, MITSUISHI Mamoru

    Regulatory Science of Medical Products   Vol. 9 ( 1 ) page: 31 - 36   2019

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    <p>Recent advances in artificial intelligence (AI) are propelling the development of AI-based medical and healthcare devices and systems. AI-based medical systems have new characteristics to be considered by developers and reviewers, namely, plasticity causing changes in system performance through learning, unpredictability of system behavior due to the black box nature of the AI process, and impact of advanced autonomy of AI-based medical systems on the relationship between patients and doctors. New research-and-development and medical-device-reviewing platforms need to be urgently discussed to prepare for up-coming new AI-based medical systems by considering aforementioned characteristics.</p>

    DOI: 10.14982/rsmp.9.31

    CiNii Research

  411. 大腸内視鏡診断への人工知能応用:Endocytoを用いた診断支援システムの研究開発経験から

    森 悠一, 工藤 進英, 森 健策

    日本消化器病学会雑誌   Vol. 115 ( 12 ) page: 1030-1036   2018.12

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  412. 大腸内視鏡治療誘導のためのRecurrent Neural Networkを用いた大腸内視鏡トラッキング手法の開発 Reviewed

    小田 昌宏, Holger R. Roth, 北坂 孝幸, 古川 和宏, 宮原 良二, 廣岡 芳樹, Nassir Navab, 森 健策

    日本バーチャルリアリティ学会論文誌   Vol. 23 ( 4 ) page: 249-252   2018.12

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    DOI: 10.18974/tvrsj.23.4_249

  413. Micro CT and Histopathological Image Registration Based on Deep-Learning Assisted Image Registration

    Kensaku Mori, Kai Nagara, Shota Nakamura, Hirohisa Oda, MENG, Holger R. Roth,, Masahiro Oda

    RSNA2018     page: CH218-ED-X   2018.11

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  414. Computer Assistance in Comparison of Kidney Function Variation Between Pre- and Post-nephrectomy

    Chenglong Wang, Masahiro Oda, Jun Nagayama, Yasushi Yoshino, Tokunori Yamamoto, Kensaku Mori

    RSNA2018     page: UR007-EB-WEA   2018.11

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  415. 3D High-Resolution Microstructure Imaging of the Heart

    Hirohisa Oda, MENG, Holger R. Roth,, Naoki Sunaguch, Tetsuya Yuasa, Toshiaki Akita, Kensaku Mori, Daisuke Shimao, Shu Ichihara, Masami Ando, Noriko Usami, Masahiro Oda, Yuji Narita

    RSNA2018     page: CA001-EC-X   2018.11

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  416. 病院内位置測位手法の検討

    山下 佳子, 大山 慎太郎, 大谷 智洋, 白鳥 義宗, 森 健策

    電子情報通信学会技術研究報告(MI), MICT2018-47   Vol. 118 ( 285 ) page: 41-44   2018.11

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  417. U‒Net を用いた腹腔鏡動画像における出血領域検出に関する検討

    小澤 卓也, 小田 紘久, 伊東 隼人, 北坂 孝幸, Holger R. Roth, 小田 昌宏, 林 雄一郎, 三澤 一成, 伊藤 雅昭, 竹下 修由 , 森 健策

    日本コンピュータ外科学会誌 第27回日本コンピュータ外科学会大会特集号   Vol. 20 ( 4 18(6)-10 ) page: 370   2018.11

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  418. ディープラーニングを用いた腹腔鏡映像からの腹腔鏡下胃切除術の手術工程解析の検討

    林 雄一郎, 杉野 貴明, 小田 昌宏, 三澤 一成, 森 健策

    日本コンピュータ外科学会誌 第27回日本コンピュータ外科学会大会特集号   Vol. 20 ( 4 18(ⅩⅠ)-10 ) page: 368-369   2018.11

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  419. 腹腔鏡把持ロボットのための自動ハンドアイキャリブレーションの検討

    蒋 帥, 林 雄一郎, 小田 昌宏, 北坂 孝幸, 三澤 一成, 森 健策

    日本コンピュータ外科学会誌 第27回日本コンピュータ外科学会大会特集号   Vol. 20 ( 4 18(Ⅹ)-10 ) page: 359   2018.11

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  420. イレウス診断支援システムにおける閉塞部位の誤検出修正及び改善ツールの構築

    西尾 光平, 小田 紘久, 千馬 耕亮, 北坂 孝幸, Holger R. Roth, 伊東 隼人, 林 雄一郎, 小田 昌宏, 檜 顕成, 内田 広夫, 森 健策

    日本コンピュータ外科学会誌 第27回日本コンピュータ外科学会大会特集号   Vol. 20 ( 4 18(Ⅹ)-3 ) page: 348-349   2018.11

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  421. SLAM ベースのビジュアルトラッキングにおける隣接フレーム利用再構成手法の評価

    王 成, 小田 昌宏, 林 雄一郎, 北坂 孝幸, 本間 裕敏, 高畠 博嗣, 森 雅樹, 名取 博, 森 健策

    日本コンピュータ外科学会誌 第27回日本コンピュータ外科学会大会特集号   Vol. 20 ( 4 18(Ⅸ)-4 ) page: 342-343   2018.11

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  422. ステレオ手術顕微鏡画像からの脳表の 3 次元形状復元と術前 MRI 画像との融合による脳神経外科手術支援の検討

    林 雄一郎, 藤井 正純, 柴田 睦実, Dilip Bhandari, 森 健策

    日本コンピュータ外科学会誌 第27回日本コンピュータ外科学会大会特集号   Vol. 20 ( 4 18(Ⅷ)-4 ) page: 334-335   2018.11

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  423. CT 像より自動抽出された動脈領域に対応した機械学習に基づく腹部動脈血管名自動命名法

    鉄村 悠介, 林 雄一郎, 小田 昌宏, 北坂 孝幸, 三澤 一成, 森 健策

    日本コンピュータ外科学会誌 第27回日本コンピュータ外科学会大会特集号   Vol. 20 ( 4 18(Ⅶ)-4 ) page: 322-323   2018.11

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  424. 生成モデルを利用したマイクロ CT 画像の半教師ありセグメンテーション

    守谷 享泰, Holger R. Roth, 中村 彰太, 小田 紘久, 小田 昌宏, 森 健策

    日本コンピュータ外科学会誌 第27回日本コンピュータ外科学会大会特集号   Vol. 20 ( 4 18(Ⅵ)-9 ) page: 312-313   2018.11

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  425. 不均衡データセットからの学習データセット構築法 ―機械学習に基づく医用画像分類に向けて―

    伊東 隼人, 森 悠一, 三澤 将史, 小田 昌宏, 工藤 進英, 森 健策

    日本コンピュータ外科学会誌 第27回日本コンピュータ外科学会大会特集号   Vol. 20 ( 4 18(Ⅲ)-5 ) page: 261-262   2018.11

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  426. 深層学習を用いた屈折 X 線 CT 画像からの眼球構造抽出 ―Sparse annnotation データの学習法に関する検討―

    杉野 貴明, Holger R. Roth, 小田 昌宏, 砂口 尚輝, 島雄 大介, 森 健策

    日本コンピュータ外科学会誌 第27回日本コンピュータ外科学会大会特集号   Vol. 20 ( 4 18(Ⅲ)-4 ) page: 259-260   2018.11

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  427. 深層学習を用いたマイクロ CT 画像の超解像に関する初期的検討

    鄭 通, Holger R. Roth, 小田 昌宏, 小田 紘久, 中村 彰太, 森 健策

    日本コンピュータ外科学会誌 第27回日本コンピュータ外科学会大会特集号   Vol. 20 ( 4 18(Ⅱ)-8 ) page: 252-253   2018.11

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  428. 医用画像処理のための深層学習サンプルコード集 DMED

    小田 昌宏, 原 武史, 森 健策

    日本コンピュータ外科学会誌 第27回日本コンピュータ外科学会大会特集号   Vol. 20 ( 4 18(Ⅱ)-5 ) page: 248-249   2018.11

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  429. 超拡大内視鏡におけるAI

    森 健策, 伊東 隼人, 三澤 将史, 森 悠一, 工藤 進英

    日本光学会年次学術講演会講演予稿集     page: 226-227   2018.11

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  430. Investigation on the condition of using adjacent reconstruction in visual bronchoscope tracking

      Vol. 118 ( 286 ) page: 27-32   2018.11

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  431. 3D High-Resolution Microstructure Imaging of the Heart

    Hirohisa Oda, MENG, Holger R. Roth, Naoki Sunaguch, Tetsuya Yuasa, Toshiaki Akita, Kensaku Mori, Daisuke Shimao, Shu Ichihara, Masami Ando, Noriko Usami, Masahiro Oda, Yuji Narita

    RSNA2018     page: CA001-EC-X   2018.11

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  432. Computer Assistance in Comparison of Kidney Function Variation Between Pre- and Post-nephrectomy

    Chenglong Wang, Masahiro Oda, Jun Nagayama, Yasushi Yoshino, Tokunori Yamamoto, Kensaku Mori

    RSNA2018     page: UR007-EB-WEA   2018.11

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  433. CT 像より自動抽出された動脈領域に対応した機械学習に基づく腹部動脈血管名自動命名法

    鉄村 悠介, 林 雄一郎, 小田 昌宏, 北坂 孝幸, 三澤 一成, 森 健策

    日本コンピュータ外科学会誌 第27回日本コンピュータ外科学会大会特集号   Vol. 20 ( 4 18(Ⅶ)-4 ) page: 322-323   2018.11

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  434. DRINet for Medical Image Segmentation Reviewed

    Liang Chen, Paul Bentley, Kensaku Mori, Kazunari Misawa, Michitaka Fujiwara, Daniel Rueckert

    IEEE Transaction on Medical Imaging   Vol. 37 ( 11 ) page: 2453-2462   2018.10

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  435. Real-Time Use of Artificial Intelligence in Identification of Diminutive Polyps During Colonoscopy: A Prospective Study Reviewed

    Yuichi Mori, Shin-ei Kudo, Masashi Misawa, Yutaka Saito, Hiroaki Ikematsu, Kinichi Hotta, Kazuo Ohtsuka, Fumihiko Urushibara, Shinichi Kataoka, Yushi Ogawa, Yasuharu Maeda, Kenichi Takeda, Hiroki Nakamura, Katsuro Ichimasa, Toyoki Kudo, Takemasa Hayashi, Kunihiko Wakamura, Fumio Ishida, Haruhiro Inoue, Hayato Itoh, Masahiro Oda, Kensaku Mori

    Annals of Internal Medicine     2018.9

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    DOI: 10.7326/M18-0249

  436. Application of three-dimensional print in minor hepatectomy following liver partition between anterior and posterior sectors Reviewed

    Tsuyoshi Igami, Yoshihiko Nakamura, Masahiro Oda, Hiroshi Tanaka, Motoi Nojiri, Tomoki Ebata, Yukihiro Yokoyama, Gen Sugawara, Takashi Mizuno, Junpei Yamaguchi, Kensaku Mori, Masato Nagino

    ANZ Journal of Surgery   Vol. 88 ( 9 ) page: 882-885   2018.9

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  437. Fully Convolutional Network-based Eyeball Segmentation from Sparse Annotation for Eye Surgery Simulation Model

    Takaaki Sugino, Holger R. Roth, Masahiro Oda, Kensaku Mori

    International Workshop on Bio-Imaging and Visualization for Patient-Customized Simulations, BIVPCS 2018, LNCS 11042     page: 118-126   2018.9

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  438. Fully automated diagnostic system with artificial intelligence using endocytoscopy to identify the presence of histologic inflammation associated with ulcerative colitis (with video) Reviewed

    Yasuharu Maeda, Shin-eiKudo, Yuichi Mori, Masashi Misawa, Noriyuki Ogata, Seiko Sasanuma, Kunihiko Wakamura, Masahiro Oda, Kensaku Mori, Kazuo Ohtsuka

    Gastrointestinal endoscopy     2018.9

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    DOI: 10.1016/j.gie.2018.09.024

  439. A Multi-scale Pyramid of 3D Fully Convolutional Networks for Abdominal Multiorgan Segmentation

    Holger Roth, Chen Shen, Hirohisa Oda, Takaaki Sugino, Masahiro Oda, Yuichiro Hayashi, Kazunari Misawa, Kensaku Mori

    MICCAI 2018, LNCS 11073     page: 417-425   2018.9

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  440. BESNet: Boundary-enhanced Segmentation of Cells in Histopathological Images

    Hirohisa Oda, Holger Roth, Kosuke Chiba, Jure Sokolic, Takayuki Kitasaka, Masahiro Oda, Akinari Hinoki, Hiroo Uchida, Julia A Schnabel, Kensaku Mori

    MICCAI 2018, LNCS 11071     page: 228-236   2018.9

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  441. Towards Automated Colonoscopy Diagnosis: Binary Polyp Size Estimation via Unsupervised Depth Learning

    Hayato Itoh, Holger Roth, Le Lu, Masahiro Oda, Masashi Misawa, Yuichi Mori, Shin-ei Kudo, Kensaku Mori

    MICCAI 2018, LNCS 11071     page: 176-184   2018.9

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  442. Colon Shape Estimation Method for Colonoscope Tracking using Recurrent Neural Networks

    Masahiro Oda, Holger Roth, Takayuki Kitasaka, Kazuhiro Furukawa, Ryoji Miyahara, Yoshiki Hirooka, Hidemi Goto, Nassir Navab, Kensaku Mori

    MICCAI 2018, LNCS 11073     page: 176-184   2018.9

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  443. BESNet: Boundary-enhanced Segmentation of Cells in Histopathological Images

    Hirohisa Oda, Holger Roth, Kosuke Chiba, Jure Sokolic, Takayuki Kitasaka, Masahiro Oda, Akinari Hinoki, Hiroo Uchida, Julia A Schnabel, Kensaku Mori

    MICCAI 2018, LNCS 11071     page: 228-236   2018.9

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  444. A Multi-scale Pyramid of 3D Fully Convolutional Networks for Abdominal Multiorgan Segmentation

    Holger Roth, Chen Shen, Hirohisa Oda, Takaaki Sugino, Masahiro Oda, Yuichiro Hayashi, Kazunari Misawa, Kensaku Mori

    MICCAI 2018, LNCS 11073     page: 417-425   2018.9

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  445. Application of three-dimensional print in minor hepatectomy following liver partition between anterior and posterior sectors Reviewed

    Tsuyoshi Igami, Yoshihiko Nakamura, Masahiro Oda, Hiroshi Tanaka, Motoi Nojiri, Tomoki Ebata, Yukihiro Yokoyama, Gen Sugawara, Takashi Mizuno, Junpei Yamaguchi, Kensaku Mori, Masato Nagino

    ANZ Journal of Surgery   Vol. 88 ( 9 ) page: 882-885   2018.9

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  446. Anatomical location classification of gastroscopic images using DenseNet trained from Cyclical Learning Rate

    Qier Meng, Kiyohito Tanaka, Shin'ichi Satoh, Masaru Kitsuregawa, Yusuke Kurose, Tatsuya Harada, Hideaki Hayashi, Ryoma Bise, Seiichi Uchida, Masahiro Oda, Kensaku Mori

        page: PS1-51   2018.8

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  447. Sparse annotationによる深層学習ベースの解剖構造抽出:屈折X線CT像からの精密な眼球セグメンテーション

    杉野貴明,Holger R. Roth,小田昌宏,砂口尚輝,島雄大介,市原周,湯浅哲也,安藤正海,森健策

    MIRU2018     page: PS3-11   2018.8

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  448. 超拡大内視鏡における病理画像分類のための特徴選択法

    伊東 隼人, 森 悠一, 三澤 将史, 小田 昌宏, 工藤 進英, 森 健策

    MIRU2018     page: PS2-17   2018.8

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  449. Radiomics nomogram for predicting the malignant potential of gastrointestinal stromal tumours preoperatively Reviewed

    Tao Chen, Zhenyuan Ning, Lili Xu, Xingyu Feng, Shuai Han, Holger R. Roth, Wei Xiong, Xixi Zhao, Yanfeng Hu, Hao Liu, Jiang Yu, Yu Zhang, Yong Li, Yikai Xu, Kensaku Mori, Guoxin Li

    European radiology     page: 1-9   2018.8

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    DOI: 10.1007/s00330-018-5629-2

  450. Anatomical location classification of gastroscopic images using DenseNet trained from Cyclical Learning Rate

    Qier Meng, Kiyohito Tanaka, Shin'ichi Satoh, Masaru Kitsuregawa, Yusuke Kurose, Tatsuya Harada, Hideaki Hayashi, Ryoma Bise, Seiichi Uchida, Masahiro Oda, Kensaku Mori

        page: PS1-51   2018.8

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  451. Attention U-Net: Learning Where to Look for the Pancreas

    Ozan Oktay, Jo Schlemper, Loic Le Folgoc, Matthew Lee, Mattias Heinrich, Kazunari Misawa, Kensaku Mori, Steven McDonagh?, Nils Y. Hammerla, Bernhard Kainz, Ben Glocker, Daniel Rueckert

    Medical Imaging with Deep Learning MIDL2018     page: 00   2018.7

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  452. マイクロCT画像からのRSTを用いた小葉壁抽出手法の検討

    趙 笑添, Holger R. Roth, 中村彰太, 小田紘久, 林 雄一郎, 守谷享泰, 長柄 快, 小田昌宏, 森 健策

    電子情報通信学会技術研究報告(MI)   Vol. 118 ( 150 ) page: 11-16   2018.7

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  453. Fast Marching Algorithmに基づく小児CT像からの腸管閉塞部位検出手法

    西尾 光平, 小田 紘久, 千馬 耕亮, 北坂 孝幸, Holger Roth, 伊東 隼人, 小田 昌宏, 檜 顕成, 内田 広夫, 森 健策

    第37回日本医用画像工学会大会予稿集     page: OP1-6   2018.7

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  454. Fully convolutional networkを用いた少量画像データ学習からの頭部解剖構造抽出

    杉野 貴明,Holger R. Roth,小田 昌宏,庄野 直之,金 太一,森 健策

    第37回日本医用画像工学会大会予稿集     page: OP1-1   2018.7

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  455. 機械学習による内視鏡動画インスタンスセグメンテーションのための手動アノテーションツールの開発

    小澤 卓也, 小田 紘久, 伊東 隼人, 北坂 孝幸, Holger R. Roth, 小田 昌宏, 林 雄一郎, 三澤 一成, 伊藤 雅昭, 竹下 修由, 森 健

    第37回日本医用画像工学会大会予稿集     page: OP1-7   2018.7

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  456. 教師なし深度推定を利用したRGB-D 特徴抽出に基づくポリープのトリナリサイズ推定

    伊東隼人, Holger Roth, 三澤将史, 森悠一, 小田昌宏, 工藤進英, 森健策

    第37回日本医用画像工学会大会予稿集     page: OP14-4   2018.7

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  457. 機械学習を用いた腹部動脈血管名自動命名における臓器情報および多血管相互関係利用方法の検討

    鉄村 悠介, Holger Roth, 林 雄一郎, 小田 昌宏, 三澤 一成, 森 健策

    第37回日本医用画像工学会大会予稿集     page: OP14-2   2018.7

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  458. 隣接復元を用いたSLAMベースの気管支鏡追跡の改善

    王 成, 小田 昌宏, 林 雄一郎, 本間 裕敏, 高畑 博嗣, 森 雅樹, 名取 博, 森 健策

    第37回日本医用画像工学会大会予稿集     page: OP13-6   2018.7

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  459. 腹腔鏡下手術のためのVR 手術再観察システムの開発

    鈴木 拓矢,道満 恵介,目加田 慶人,三澤 一成,森 健策

    第37回日本医用画像工学会大会予稿集     page: OP8-4   2018.7

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  460. 脳神経外科手術支援のための手術顕微鏡画像からの脳表の3次元形状復元に関する検討

    林 雄一郎, 柴田 睦実, 藤井 正純, 森 健策

    第37回日本医用画像工学会大会予稿集     page: OP8-1   2018.7

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  461. Fully convolutional networkを用いた小構造物セグメンテーション方法の検討及び腹部動脈への適用

    小田 昌宏, Holger R. Roth, 北坂 孝幸, 三澤 一成, 藤原 道隆, 森 健策

    第37回日本医用画像工学会大会予稿集     page: OP7-5   2018.7

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  462. 胃の変形情報を利用した経時リンパ節の自動対応付け手法の精度向上に関する研究

    舘 高基, 小田 昌宏, 林 雄一郎, 中村 嘉彦, 北坂 孝幸, 三澤 一成, 森 健策

    第37回日本医用画像工学会大会予稿集     page: OP4-2   2018.7

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  463. ウサギ心臓の屈折CT像における線維配向の可視化ならびに評価

    小田 紘久, Holger R. Roth, 砂口 尚輝, 宇佐美 紀子, 小田 昌宏 , 島雄 大介, 市原 周, 湯浅 哲也, 安藤 正海, 秋田 利明, 成田 裕司, 森 健策

    第37回日本医用画像工学会大会予稿集     page: OP1-8   2018.7

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  464. Deformation matching of laparoscopic gastrectomy Navigation based on finite element analysis

    T. Chen, G. Wei, W. Shi, Y.Hu, J. Yu, Z.Jiang,K. Mori

    International Journal of Computer Assisted Radiology and Surgery   Vol. 13 ( 1 ) page: s67-68   2018.6

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  465. Unsupervised deep learning based registration for aligning micro CT and histology images

    K. Nagara, S. Nakamura, H. Roth, M. Oda, K. Mori

    International Journal of Computer Assisted Radiology and Surgery   Vol. 13 ( 1 ) page: s155-157   2018.6

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  466. Micro-focus X-ray CT of the heart:A comparison with X-ray refraction-contrast CT,

    Hirohisa Oda, Holger R. Roth, Naoki Sunaguchi, Daisuke Shimao, Takaaki Sugino, Masahiro Oda, Toshiaki Akita, Yuji Narita, Shu Ichihara, Tetsuya Yuasa, Masami Ando, Kensaku Mori

    International Journal of Computer Assisted Radiology and Surgery   Vol. 13 ( 1 ) page: s140-142   2018.6

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  467. Polyp detection in colonoscopic videos by using spatio-temporal feature

    Hayato Itoh, Holger R. Roth, Masashi Misawa, Yuichi Mori, Masahiro Oda, Shin-ei Kudo, Kensaku Mori

    International Journal of Computer Assisted Radiology and Surgery   Vol. 13 ( 1 ) page: s97-98   2018.6

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  468. Deformation matching of laparoscopic gastrectomy Navigation based on finite element analysis

    T. Chen, G. Wei, W. Shi, Y.Hu, J. Yu, Z.Jiang, K. Mori

    International Journal of Computer Assisted Radiology and Surgery   Vol. 13 ( 1 ) page: s67-68   2018.6

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  469. Unsupervised deep learning based registration for aligning micro CT and histology images

    K. Nagara, S. Nakamura, H. Roth, M. Oda, K. Mori

    International Journal of Computer Assisted Radiology and Surgery   Vol. 13 ( 1 ) page: s155-157   2018.6

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  470. Micro-focus X-ray CT of the heart:A comparison with X-ray refraction-contrast CT,

    Hirohisa Oda, Holger R. Roth, Naoki Sunaguchi, Daisuke Shimao, Takaaki Sugino, Masahiro Oda, Toshiaki Akita, Yuji Narita, Shu Ichihara, Tetsuya Yuasa, Masami Ando, Kensaku Mori

    International Journal of Computer Assisted Radiology and Surgery   Vol. 13 ( 1 ) page: s140-142   2018.6

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  471. Polyp detection in colonoscopic videos by using spatio-temporal feature

    Hayato Itoh, Holger R. Roth, Masashi Misawa, Yuichi Mori, Masahiro Oda, Shin-ei Kudo, Kensaku Mori

    International Journal of Computer Assisted Radiology and Surgery   Vol. 13 ( 1 ) page: s97-98   2018.6

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  472. Improvement of robustness of SLAM-based bronchoscope tracking by posture guided feature matching

    Cheng Wang,Masahiro Oda,Yuichiro Hayashi,Hirotoshi Honma, Hirotsugu Takabatake, Masaki Mori, Hiroshi Natori, Kensaku Mori

    International Journal of Computer Assisted Radiology and Surgery   Vol. 13 ( 1 ) page: s11-12   2018.6

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  473. Auto-context 3D fully convolutional networks for multi-scale semantic segmentation of abdominal CT volumes

    K. Mori, H. Roth, C. Shen, H. Oda, T. Sugino, M. Oda, Y. Hayashi, K. Misawa

    International Journal of Computer Assisted Radiology and Surgery   Vol. 13 ( 1 ) page: s18-19   2018.6

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  474. Unsupervised 3D micro-CT image segmentation based on a hybrid of VAE and GAN

    T. Moriya, H. Roth, S. Nakamura, H. Oda, K. Nagara, M. Oda, K. Mori

    International Journal of Computer Assisted Radiology and Surgery   Vol. 13 ( 1 ) page: s15-17   2018.6

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  475. Abdominal artery segmentation from CT volumes using fully convolutional network for small artery segmentation

    Masahiro Oda, Takayuki Kitasaka, Kazunari Misawa, Michitaka Fujiwara, Kensaku Mori,

    International Journal of Computer Assisted Radiology and Surgery   Vol. 13 ( 1 ) page: s20-21   2018.6

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  476. Eye structure segmentation on micro-CT images using 3D fully convolutional network with sparsely-annotated training data

    T. Sugino, H. Roth, M. Oda, S. Omata, S. Sakuma, F. Arai, K. Mori

    International Journal of Computer Assisted Radiology and Surgery   Vol. 13 ( 1 ) page: s182-184   2018.6

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  477. Automated ganglion cell detection using fully convolutional networks and evaluation under different training losses

    Hirohisa Oda, Kosuke Chiba, Holger R. Roth, Takayuki Kitasaka, Masahiro Oda, Akinari Hinoki, Hiroo Uchida, Kensaku Mori

    International Journal of Computer Assisted Radiology and Surgery   Vol. 13 ( 1 ) page: s104-106   2018.6

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  478. Semi-supervised spherical K-means for segmenting idiopathic interstitial pneumonia from chest CT images

    C. Wang, T. Moriya, Y. Hayashi, M. Oda, H. Ohkubo, K. Mori

    International Journal of Computer Assisted Radiology and Surgery   Vol. 13 ( 1 ) page: s27-28   2018.6

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  479. Evaluation of 3D fully convolutional networks for multi-class organ segmentation in contrast-enhanced CT

    Chen Shen, Holger R. Roth, Hirohisa Oda, Masahiro Oda, Yuichiro Hayashi, Kazunari Misawa, Tadaaki Miyamoto, and Kensaku Mori

    International Journal of Computer Assisted Radiology and Surgery   Vol. 13 ( 1 ) page: s21-22   2018.6

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  480. Abdominal artery segmentation from CT volumes using fully convolutional network for small artery segmentation

    Masahiro Oda, Takayuki Kitasaka, Kazunari Misawa, Michitaka Fujiwara, Kensaku Mori

    International Journal of Computer Assisted Radiology and Surgery   Vol. 13 ( 1 ) page: s20-21   2018.6

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  481. Automated ganglion cell detection using fully convolutional networks and evaluation under different training losses

    Hirohisa Oda, Kosuke Chiba, Holger R. Roth, Takayuki Kitasaka, Masahiro Oda, Akinari Hinoki, Hiroo Uchida, Kensaku Mori

    International Journal of Computer Assisted Radiology and Surgery   Vol. 13 ( 1 ) page: s104-106   2018.6

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  482. Auto-context 3D fully convolutional networks for multi-scale semantic segmentation of abdominal CT volumes

    K. Mori, H. Roth, C. Shen, H. Oda, T. Sugino, M. Oda, Y. Hayashi, K. Misawa

    International Journal of Computer Assisted Radiology and Surgery   Vol. 13 ( 1 ) page: s18-19   2018.6

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  483. Evaluation of 3D fully convolutional networks for multi-class organ segmentation in contrast-enhanced CT

    Chen Shen, Holger R. Roth, Hirohisa Oda, Masahiro Oda, Yuichiro Hayashi, Kazunari Misawa, Tadaaki Miyamoto, Kensaku Mori

    International Journal of Computer Assisted Radiology and Surgery   Vol. 13 ( 1 ) page: s21-22   2018.6

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  484. Improvement of robustness of SLAM-based bronchoscope tracking by posture guided feature matching

    Cheng Wang, Masahiro Oda, Yuichiro Hayashi, Hirotoshi Honma, Hirotsugu Takabatake, Masaki Mori, Hiroshi Natori, Kensaku Mori

    International Journal of Computer Assisted Radiology and Surgery   Vol. 13 ( 1 ) page: s11-12   2018.6

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  485. Eye structure segmentation on micro-CT images using 3D fully convolutional network with sparsely-annotated training data

    T. Sugino, H. Roth, M. Oda, S. Omata, S. Sakuma, F. Arai, K. Mori

    International Journal of Computer Assisted Radiology and Surgery   Vol. 13 ( 1 ) page: s182-184   2018.6

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  486. Semi-supervised spherical K-means for segmenting idiopathic interstitial pneumonia from chest CT images

    C. Wang, T. Moriya, Y. Hayashi, M. Oda, H. Ohkubo, K. Mori

    International Journal of Computer Assisted Radiology and Surgery   Vol. 13 ( 1 ) page: s27-28   2018.6

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  487. Unsupervised 3D micro-CT image segmentation based on a hybrid of VAE and GAN

    T. Moriya, H. Roth, S. Nakamura, H. Oda, K. Nagara, M. Oda, K. Mori

    International Journal of Computer Assisted Radiology and Surgery   Vol. 13 ( 1 ) page: s15-17   2018.6

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  488. Artificial Intelligence-Assisted Polyp Detection for Colonoscopy: Initial Experience Reviewed

    Masashi Misawa, Shin-eiKudo, Yuichi Mori, Tomonari Cho, Shinichi Kataoka, Akihiro Yamauchi, Yushi Ogawa, Yasuharu Maeda, Kenichi Takeda, Katsuro Ichimasa, Hiroki Nakamura, Yusuke Yagawa, Naoya Toyoshima, Noriyuki Ogata, Toyoki Kudo, Tomokazu Hisayuki, Takemasa Hayashi, Kunihiko Wakamura, Toshiyuki Baba, Fumio Ishida, Hayato Ito, Roth Holger, Kensaku Mori

    Gastroenterology   Vol. 154 ( 8 ) page: 2027-2029   2018.6

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    DOI: 10.1053/j.gastro.2018.04.003

  489. An application of cascaded 3D fully convolutional networks for medical image segmentation Reviewed

    Holger R. Roth, Hirohisa Oda, Xiangrong Zhou, Natsuki Shimizu, Ying Yang, Yuichiro Hayashi, Masahiro Oda, Michitaka Fujiwara, Kazunari Misawa, Kensaku Mori

    Computerized Medical Imaging and Graphics   Vol. 66   page: 90 - 99   2018.6

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    Recent advances in 3D fully convolutional networks (FCN) have made it feasible to produce dense voxel-wise predictions of volumetric images. In this work, we show that a multi-class 3D FCN trained on manually labeled CT scans of several anatomical structures (ranging from the large organs to thin vessels) can achieve competitive segmentation results, while avoiding the need for handcrafting features or training class-specific models. To this end, we propose a two-stage, coarse-to-fine approach that will first use a 3D FCN to roughly define a candidate region, which will then be used as input to a second 3D FCN. This reduces the number of voxels the second FCN has to classify to ∼10% and allows it to focus on more detailed segmentation of the organs and vessels. We utilize training and validation sets consisting of 331 clinical CT images and test our models on a completely unseen data collection acquired at a different hospital that includes 150 CT scans, targeting three anatomical organs (liver, spleen, and pancreas). In challenging organs such as the pancreas, our cascaded approach improves the mean Dice score from 68.5 to 82.2%, achieving the highest reported average score on this dataset. We compare with a 2D FCN method on a separate dataset of 240 CT scans with 18 classes and achieve a significantly higher performance in small organs and vessels. Furthermore, we explore fine-tuning our models to different datasets. Our experiments illustrate the promise and robustness of current 3D FCN based semantic segmentation of medical images, achieving state-of-the-art results.1

    DOI: 10.1016/j.compmedimag.2018.03.001

    Web of Science

    Scopus

    PubMed

  490. Port placement planning method for assistant surgeon in laparoscopic gastrectomy

    Y. Hayashi, K. Misawa, K. Mori

    International Journal of Computer Assisted Radiology and Surgery   Vol. 13 ( 1 ) page: s231-232   2018.6

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  491. Port placement planning method for assistant surgeon in laparoscopic gastrectomy

    Y. Hayashi, K. Misawa, K. Mori

    International Journal of Computer Assisted Radiology and Surgery   Vol. 13 ( 1 ) page: s231-232   2018.6

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  492. Regulatory Science on AI-based Medical Devices and Systems Reviewed

    Kiyoyuki Chinzei, Akinobu Shimizu, Kensaku Mori, Kanako Harada, Hideaki Takeda, Makoto Hashizume, Mayumi Ishizuka, Nobumasa Kato, Ryuzo Kawamori, Shunei Kyo, Kyosuke Nagata, Takashi Yamane, Ichiro Sakuma, Kazuhiko Ohe, Mamoru Mitsuishi

    Advanced Biomedical Engineering   Vol. 7   page: 118-123   2018.5

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    DOI: org/10.14326/abe.7.118

  493. デスクトップ型マイクロCTによる微細解剖構造イメージング

    森 健策

    Medical Imaging Technology   Vol. 36 ( 3 ) page: 127-132   2018.5

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  494. 特集/マイクロ解剖学のための微細解剖構造イメージング

    森 健策

    Medical Imaging Technology   Vol. 36 ( 3 ) page: 105-106   2018.5

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  495. DRINet for Medical Image Segmentation Reviewed

    Liang Chen, Paul Bentley, Kensaku Mori, Kazunari Misawa, Michitaka Fujiwara, Daniel Rueckert

    IEEE Transaction on Medical Imaging     2018.5

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    DOI: 10.1109/TMI.2018.2835303

  496. Potential of artificial intelligence-assisted colonoscopy using an endocytoscope (with video) Reviewed

    Yuichi Mori, Shin-ei Kudo, Kensaku Mori

    Digestive Endoscopy   Vol. 30 ( S1 ) page: 52-53   2018.4

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    DOI: org/10.1111/den.13005

  497. Cascaded 3D Fully Convolutional Networks for Medical Image Segmentation

    Holger Roth, Hirohisa Oda, Xiangrong Zhou, Natsuki Shimizu, Ying Yang, Chen Shen, Yuichiro Hayashi, Masahiro Oda, Michitaka Fujiwara, Kazunari Misawa, Kensaku Mori,

    GTC2018,     page: S8532   2018.3

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  498. Cascaded 3D Fully Convolutional Networks for Medical Image Segmentation

    Holger Roth, Hirohisa Oda, Xiangrong Zhou, Natsuki Shimizu, Ying Yang, Chen Shen, Yuichiro Hayashi, Masahiro Oda, Michitaka Fujiwara, Kazunari Misawa, Kensaku Mori

    GTC2018,     page: S8532   2018.3

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  499. ディープラーニングを用いた教師なし学習によるレジストレーション手法の初期的検討

    長柄 快, Holger R. Roth, 中村彰太, 小田昌宏, 森 健策

    電子情報通信学会技術研究報告(MI)   Vol. 117 ( 518 ) page: 7-12   2018.3

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  500. MICCAI2017参加報告

    大竹義人, 伊藤康一, 小田昌宏, 備瀬竜馬, 諸岡健一, 周 向栄, 斉藤 篤, 清水昭伸, 増谷佳孝, 佐藤嘉伸, 森 健策

    電子情報通信学会技術研究報告(MI)   Vol. 117 ( 518 ) page: 125-131   2018.3

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  501. 複数のステレオ内視鏡画像からの臓器形状復元の定量評価

    柴田睦実, 林 雄一郎, 小田昌宏, 三澤一成, 森 健策

    電子情報通信学会技術研究報告(MI)   Vol. 117 ( 518 ) page: 117-122   2018.3

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  502. CNNによる回帰を用いた臓器領域の位置推定手法の初期的検討

    清水南月, 小田昌宏, ロス ホルガー, 林 雄一郎, 三澤一成, 藤原道隆, 森 健策

    電子情報通信学会技術研究報告(MI)   Vol. 117 ( 518 ) page: 81-86   2018.3

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  503. 3D U-Netと測地距離カーネルを取り入れた全連結条件付き確率場に基づく医用画像からの多臓器自動抽出

    楊 瀛, Roth Holger, 小田昌宏, 北坂孝幸, 三澤一成, 森 健策

    電子情報通信学会技術研究報告(MI)   Vol. 117 ( 518 ) page: 75-80   2018.3

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  504. 超拡大内視鏡画像における腫瘍性ポリープ分類に向けたグラスマン距離に基づく特徴選択法

    伊東隼人, 森 悠一, 三澤将史, 小田昌宏, 工藤進英, 森 健策

    電子情報通信学会技術研究報告(MI)   Vol. 117 ( 518 ) page: 51-56   2018.3

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  505. 開腹手術映像における遮蔽物除去手法の改善 FFDによる位置合わせ精度の評価

    北坂孝幸, 奥田透生, 佐藤 準, 豊田誠仁, 澤野弘明, 末永康仁, 三澤一成, 森 健策

    電子情報通信学会技術研究報告(MI)   Vol. 117 ( 518 ) page: 31-32   2018.3

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  506. Pre/intra-operative diagnosis and navigational assistance based on multidisciplinary computational anatomy

    Kensaku Mori, Masahiro Oda, Holger R roth, Yoshihiko Nakamura, Yoshito Mekada, Takayuki Kitasaka, Kazunari Misawa, Michitaka Fujiwara, Kazuhiro Durukawa, Shu Ichihara

        page: 87-105   2018.3

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  507. Artificial intelligence may help in predicting the need for additional surgery after endoscopic resection of T1 colorectal cancer. Reviewed International journal

    Katsuro Ichimasa, Shin-Ei Kudo, Yuichi Mori, Masashi Misawa, Shingo Matsudaira, Yuta Kouyama, Toshiyuki Baba, Eiji Hidaka, Kunihiko Wakamura, Takemasa Hayashi, Toyoki Kudo, Tomoyuki Ishigaki, Yusuke Yagawa, Hiroki Nakamura, Kenichi Takeda, Amyn Haji, Shigeharu Hamatani, Kensaku Mori, Fumio Ishida, Hideyuki Miyachi

    Endoscopy   Vol. 50 ( 3 ) page: 230 - 240   2018.3

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    BACKGROUND AND STUDY AIMS: Decisions concerning additional surgery after endoscopic resection of T1 colorectal cancer (CRC) are difficult because preoperative prediction of lymph node metastasis (LNM) is problematic. We investigated whether artificial intelligence can predict LNM presence, thus minimizing the need for additional surgery. PATIENTS AND METHODS: Data on 690 consecutive patients with T1 CRCs that were surgically resected in 2001 - 2016 were retrospectively analyzed. We divided patients into two groups according to date: data from 590 patients were used for machine learning for the artificial intelligence model, and the remaining 100 patients were included for model validation. The artificial intelligence model analyzed 45 clinicopathological factors and then predicted positivity or negativity for LNM. Operative specimens were used as the gold standard for the presence of LNM. The artificial intelligence model was validated by calculating the sensitivity, specificity, and accuracy for predicting LNM, and comparing these data with those of the American, European, and Japanese guidelines. RESULTS: Sensitivity was 100 % (95 % confidence interval [CI] 72 % to 100 %) in all models. Specificity of the artificial intelligence model and the American, European, and Japanese guidelines was 66 % (95 %CI 56 % to 76 %), 44 % (95 %CI 34 % to 55 %), 0 % (95 %CI 0 % to 3 %), and 0 % (95 %CI 0 % to 3 %), respectively; and accuracy was 69 % (95 %CI 59 % to 78 %), 49 % (95 %CI 39 % to 59 %), 9 % (95 %CI 4 % to 16 %), and 9 % (95 %CI 4 % - 16 %), respectively. The rates of unnecessary additional surgery attributable to misdiagnosing LNM-negative patients as having LNM were: 77 % (95 %CI 62 % to 89 %) for the artificial intelligence model, and 85 % (95 %CI 73 % to 93 %; P < 0.001), 91 % (95 %CI 84 % to 96 %; P < 0.001), and 91 % (95 %CI 84 % to 96 %; P < 0.001) for the American, European, and Japanese guidelines, respectively. CONCLUSIONS: Compared with current guidelines, artificial intelligence significantly reduced unnecessary additional surgery after endoscopic resection of T1 CRC without missing LNM positivity.

    DOI: 10.1055/s-0043-122385

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  508. Correction: Artificial intelligence may help in predicting the need for additional surgery after endoscopic resection of T1 colorectal cancer Reviewed

    Katsuro Ichimasa, Shin-ei Kudo, Yuichi Mori, Masashi Misawa, Shingo Matsudaira, Yuta Kouyama, Toshiyuki Baba, Eiji Hidaka, Kunihiko Wakamura, Takemasa Hayashi, Toyoki Kudo, Tomoyuki Ishigaki, Yusuke Yagawa, Hiroki Nakamura, Kenichi Takeda, Amyn Haji, Shigeharu Hamatani, Kensaku Mori, Fumio Ishida, Hideyuki Miyach

    Endoscopy   Vol. 50 ( 3 ) page: C2   2018.3

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    DOI: 10.1055/s-0044-100290

  509. Correction: Artificial intelligence may help in predicting the need for additional surgery after endoscopic resection of T1 colorectal cancer Reviewed

    Katsuro Ichimasa, Shin-ei Kudo, Yuichi Mori, Masashi Misawa, Shingo Matsudaira, Yuta Kouyama, Toshiyuki Baba, Eiji Hidaka, Kunihiko Wakamura, Takemasa Hayashi, Toyoki Kudo, Tomoyuki Ishigaki, Yusuke Yagawa, Hiroki Nakamura, Kenichi Takeda, Amyn Haji, Shigeharu Hamatani, Kensaku Mori, Fumio Ishida, Hideyuki Miyach

    Endoscopy   Vol. 50 ( 3 ) page: C2   2018.3

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  510. Correction: Artificial intelligence may help in predicting the need for additional surgery after endoscopic resection of T1 colorectal cancer Reviewed

    Katsuro Ichimasa, Shin-ei Kudo, Yuichi Mori, Masashi Misawa, Shingo Matsudaira, Yuta Kouyama, Toshiyuki Baba, Eiji Hidaka, Kunihiko Wakamura, Takemasa Hayashi, Toyoki Kudo, Tomoyuki Ishigaki, Yusuke Yagawa, Hiroki Nakamura, Kenichi Takeda, Amyn Haji, Shigeharu Hamatani, Kensaku Mori, Fumio Ishida, Hideyuki Miyach

    Endoscopy   Vol. 50 ( 3 ) page: C2   2018.3

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  511. 3D U-Netと測地距離カーネルを取り入れた全連結条件付き確率場に基づく医用画像からの多臓器自動抽出

    楊 瀛, Roth Holger, 小田昌宏, 北坂孝幸, 三澤一成, 森 健策

    電子情報通信学会技術研究報告(MI)   Vol. 117 ( 518 ) page: 75-80   2018.3

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  512. Machine learning-based colon deformation estimation method for colonscope tracking

    Masahiro Oda, Takayuki Kitasaka, Kazuhiro Furukawa, Ryoji Miyahara, Yoshiki Hirooka, Hidemi Goto, Nassir Navab, Kensaku Mori

    Proc. SPIE 10576, Medical Imaging 2018     page: 1057619-1-1057619-6   2018.2

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  513. Dense volumetric detection and segmentation of mediastinal lymph nodes in chest CT images

    Hirohisa Oda, Holger Roth, Kanwal K. Bhatia, Masahiro Oda, Takayuki Kitasaka, Shingo Iwano, Hirotoshi Homma, Hirotsugu Takabatake, Masaki Mori, Hiroshi Natori, Julia A. Schnabel, Kensaku Mori,

    Proc. SPIE 10575, Medical Imaging 2018     page: 1057502-1-1057502-6   2018.2

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    DOI: 10.1117/12.2287066

  514. Unsupervised pathology image segmantaion using representation learning with spherical k-means

    Takayasu Moriya, Holger R. Roth, Shota Nakamura, Hirohisa Oda, Kai Nagara, Masahiro Oda, Kensaku Mori,

    Proc. SPIE 10581, Medical Imaging 2018     page: 1058111-1-1058111-7   2018.2

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    DOI: 10.1117/12.2292172

  515. Unsupervised segmentation of 3D medical images based on clustering and deep representation learning

    Takayasu Moriya, Holger R. Roth, Shota Nakamura, Hirohisa Oda, Kai Nagara, Masahiro Oda, Kensaku Mori

    Proc. SPIE 10578, Medical Imaging 2018     page: 105780-1-105780-7   2018.2

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    DOI: 10.1117/12.2293414

  516. Towards dense volumetric pancreas segmentation in CT using 3D fully convolutional network

    Holger Roth, Masahiro Oda, Natsuki Shimizu, Hirohisa Oda, Yuichiro Hayashi, Takayuki Kitasaka, Michitaka Fujiwara, Kazunari Misawa, Kensaku Mori

    Proc. SPIE 10574, Medical Imaging 2018     page: 105740B-1-105740B-6   2018.2

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    DOI: 10.1117/12.2293499

  517. Fine segmentation of tiny blood vessel based on fully-connected conditional random field

    Chenglong Wang, Masahiro Oda, Yasushi Yoshino, Tokunori Yamamoto, Kensaku Mori

    Proc. SPIE 10574, Medical Imaging 2018     page: 10740K-1-10740K-7   2018.2

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    DOI: 10.1117/12.2293486

  518. Develop and Validate a Finite Element Method Model for Deformation Matching of Laparoscopic Gastrectomy Navigation

    Tao Chena, Guodong Wei, Weili Shic, Yuichiro Hayashi, Masahiro Oda, Zhengang Jiang, Guoxin Li, Kensaku Mori

    Proc. SPIE 10576, Medical Imaging 2018     page: 105761Y-1-10576Y-6   2018.2

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  519. 医用工学と放射線技術科学との融合:期待される新技術

    戸田 尚宏, 小林 哲生, 山谷 泰賀, 有村 秀孝, 内山 良一, 森 健策, 藤田 広志, 原 武史

    日本放射線技術学会雑誌, 第73回総会学術大会シンポジウム2   Vol. 74 ( 2 ) page: 175-190   2018.2

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  520. 医用工学と放射線技術科学との融合:期待される新技術

    戸田 尚宏, 小林 哲生, 山谷 泰賀, 有村 秀孝, 内山 良一, 森 健策, 藤田 広志, 原 武史

    日本放射線技術学会雑誌, 第73回総会学術大会シンポジウム2   Vol. 74 ( 2 ) page: 175-190   2018.2

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  521. 術前術中後診断治療支援

    森 健策

    月刊「細胞]   Vol. 50 ( 1 ) page: 14-18   2018.1

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  522. 3Dプリンタの最新動向

    森 健策

    インナービジョン 2018年2月号   Vol. 33 ( 2 ) page: 35-36   2018.1

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  523. 3Dプリンタの最新動向

    森 健策

    インナービジョン 2018年2月号   Vol. 33 ( 2 ) page: 35-36   2018.1

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  524. Advanced Endoscopic Navigation: Surgical Big Data, Methodology, and Applications Reviewed

    Xiongbiao Luo, Kensaku Mori, Terry M. Peters

    Annual Review of Biomedical Engineering   Vol. 20 ( 1 ) page: 221 - 251   2018

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    Interventional endoscopy (e.g., bronchoscopy, colonoscopy, laparoscopy, cystoscopy) is a widely performed procedure that involves either diagnosis of suspicious lesions or guidance for minimally invasive surgery in a variety of organs within the body cavity. Endoscopy may also be used to guide the introduction of certain items (e.g., stents) into the body. Endoscopic navigation systems seek to integrate big data with multimodal information (e.g., computed tomography, magnetic resonance images, endoscopic video sequences, ultrasound images, external trackers) relative to the patient's anatomy, control the movement of medical endoscopes and surgical tools, and guide the surgeon's actions during endoscopic interventions. Nevertheless, it remains challenging to realize the next generation of context-aware navigated endoscopy. This review presents a broad survey of various aspects of endoscopic navigation, particularly with respect to the development of endoscopic navigation techniques. First, we investigate big data with multimodal information involved in endoscopic navigation. Next, we focus on numerous methodologies used for endoscopic navigation. We then review different endoscopic procedures in clinical applications. Finally, we discuss novel techniques and promising directions for the development of endoscopic navigation.

    DOI: 10.1146/annurev-bioeng-062117-120917

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  525. A Multi-scale Pyramid of 3D Fully Convolutional Networks for Abdominal Multi-organ Segmentation. Invited Reviewed

    Holger R. Roth, Chen Shen, Hirohisa Oda, Takaaki Sugino, Masahiro Oda, Yuichiro Hayashi, Kazunari Misawa, Kensaku Mori

    Medical Image Computing and Computer Assisted Intervention - MICCAI 2018 - 21st International Conference   Vol. 11073   page: 417 - 425   2018

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    DOI: 10.1007/978-3-030-00937-3_48

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    Other Link: https://dblp.uni-trier.de/db/conf/miccai/miccai2018-4.html#RothSOSOHMM18

  526. [Implementation of artificial intelligence into colonoscopy: experience of research and development of computer-aided diagnostic system for endocytoscopy]. Reviewed

    Mori Y, Kudo SE, Mori K

    Nihon Shokakibyo Gakkai zasshi = The Japanese journal of gastro-enterology   Vol. 115 ( 12 ) page: 1030 - 1036   2018

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    DOI: 10.11405/nisshoshi.115.1030

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  527. Application of three-dimensional print in minor hepatectomy following liver partition between anterior and posterior sectors Reviewed

    Tsuyoshi Igami, Yoshihiko Nakamura, Masahiro Oda, Hiroshi Tanaka, Motoi Nojiri, Tomoki Ebata, Yukihiro Yokoyama, Gen Sugawara, Takashi Mizuno, Junpei Yamaguchi, Kensaku Mori, Masato Nagino

    ANZ Journal of Surgery     2017.12

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    DOI: 10.1111/ans.14331

  528. Study on the Robustness of ORB-SLAM Based Outlier Elimination in Bronchoscope Tracking -- RANSAC + EPnP for Outlier Detection --

    Cheng Wang, Masahiro Oda, Yuichiro Hayashi, Hirotoshi Honma, Hirotsugu Takabatake, Masaki Mori, Hiroshi Natori, Kensaku Mori

    MI2017-58   Vol. 117 ( 281 ) page: 47-52   2017.11

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  529. On the influence of Dice loss function in multi-class organ segmentation of abdominal CT using 3D fully convolutional networks

    Chen Shen, Holger R. Roth, Hirohisa Oda, Masahiro Oda, Yuichiro Hayashi, Kazunari Misawa, Kensaku Mori

    MI2017-51   Vol. 117 ( 281 ) page: 15-20   2017.11

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  530. Machine Learning Techniques for Automated Accurate Organ Segmentation and Their Applications to Diagnosis Assistance

    Masahiro Oda, Natsuki Shimizu, Holger R. Roth, Takayuki Kitasaka, Kazunari Misawa, Kensaku Mori, Michitaka Fujiwara, Daniel Rueckert

    RSNA 2017 (Radiological Society of North America) Scientific Assembly and Annual Meeting PROGRAM IN BRIEF     page: 224   2017.11

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  531. Automated Multi-Organ Segmentation in Abdominal CT with Hierarchical 3D Fully-Convolutional Networks

    Holger R. Roth, Hirohisa Oda, MENG, Yuichiro Hayashi, Masahiro Oda, Natsuki Shimizu, Kensaku Mori, Michitaka Fujiwara, Kazunari Misawa

    RSNA 2017 (Radiological Society of North America) Scientific Assembly and Annual Meeting PROGRAM IN BRIEF     page: 267   2017.11

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  532. 3D Microstructure Visualization of Lactiferous Duct Structure Based On Refraction X-Ray CT Imaging

    Kensaku Mori, Naoki Sunaguchi, Masami Ando, Tetsuya Yuasa, Daisuke Shimao, Shu Ichihara, Rajiv Gupta

    RSNA 2017 (Radiological Society of North America) Scientific Assembly and Annual Meeting PROGRAM IN BRIEF     page: 179   2017.11

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  533. 3Dプリンターの基礎と医療応用

    森 健策

    月刊心臓   Vol. 49 ( 11 ) page: 1104-1113   2017.11

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  534. Automated Multi-Organ Segmentation in Abdominal CT with Hierarchical 3D Fully-Convolutional Networks

    Holger R. Roth, Hirohisa Oda, MENG, Yuichiro Hayashi, Masahiro Oda, Natsuki Shimizu, Kensaku Mori, Michitaka Fujiwara, Kazunari Misawa

    RSNA 2017 (Radiological Society of North America) Scientific Assembly and Annual Meeting PROGRAM IN BRIEF     page: 267   2017.11

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  535. 3Dプリンターの基礎と医療応用

    森 健策

    月刊心臓   Vol. 49 ( 11 ) page: 1104-1113   2017.11

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  536. 3D Microstructure Visualization of Lactiferous Duct Structure Based On Refraction X-Ray CT Imaging

    Kensaku Mori, Naoki Sunaguchi, Masami Ando, Tetsuya Yuasa, Daisuke Shimao, Shu Ichihara, Rajiv Gupta

    RSNA 2017 (Radiological Society of North America) Scientific Assembly and Annual Meeting PROGRAM IN BRIEF     page: 179   2017.11

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  537. サポートベクタマシンを用いたラジオミクスベースの消化管間質性腫瘍リスク評価システム

    陳 韜, 小田紘久, Holger R. Roth,北坂孝幸,小田昌宏,李 国新,森 健策

    日本コンピュータ外科学会誌 第26回日本コンピュータ外科学会大会特集号   Vol. 19 ( 4 ) page: 239-240   2017.10

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  538. Optimal port placement planning method for laparoscopic gastrectomy Reviewed

    Yuichiro Hayashi, Kazunari Misawa, Kensaku Mori

    INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY   Vol. 12 ( 10 ) page: 1677 - 1684   2017.10

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    In laparoscopic gastrectomy, as well as other laparoscopic surgery, the surgeon operates on target organs using a laparoscope and forceps inserted into the abdominal cavity through ports placed in the abdominal wall. Therefore, port placement is of vital significance in laparoscopic surgery. In this paper, we present a method for achieving optimal port placement in laparoscopic gastrectomy based on relationships between the locations of the ports and anatomical structures.
    We utilize three angle conditions to determine the optimal port placement. Proper angles for the angle conditions are calculated from measurements obtained during laparoscopic gastrectomy. The port positions determined by surgeons experienced in laparoscopic gastrectomy are measured using a three-dimensional positional tracker. The locations of the blood vessels, as well as other vital anatomical structures that are also critical in laparoscopic gastrectomy, are identified from computed tomography images. The angle relationships between the port and blood vessel locations are analyzed using the obtained positional information. Optimal port placement is determined based on the angle conditions.
    We evaluated the proposed method using the positional information obtained during 26 laparoscopic gastrectomies. Our evaluation determined that the proposed method generates optimal port placement with average errors of 22.2 and 21.2 mm in the left- and the right-hand side ports for a lead surgeon. Experienced surgeons confirmed that the optimal port placement generated by the proposed method was sufficient for clinical use.
    The proposed method provides optimal port placement in laparoscopic gastrectomy and enables a novice surgeon to determine port placement much like an experienced surgeon.

    DOI: 10.1007/s11548-017-1548-y

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  539. Automatic Segmentation of Head Anatomical Structures from Sparsely-annotated Images Reviewed

    Takaaki Sugino, Holger R. Roth, Mohammad Eshghi, Masahiro Oda, Min Suk Chung, Kensaku Mori

    IEEE International Conference on Cyborg and Bionic Systems     page: 145-149   2017.10

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  540. 機械学習を用いた腹部動脈血管名自動命名における臓器情報利用方法に関する一考察

    鉄村悠介,Holger Roth,林 雄一郎,小田昌宏,進藤幸治,大内田研宙,橋爪 誠,三澤一成, 森 健策

    日本コンピュータ外科学会誌 第26回日本コンピュータ外科学会大会特集号   Vol. 19 ( 4 ) page: 361-362   2017.10

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  541. 大腸内視鏡トラッキングのための regression forests を用いた大腸変形モデルの開発

    小田昌宏,北坂孝幸,古川和宏,宮原良二,廣岡芳樹,後藤秀実,Nassir Navabe, 森 健策

    日本コンピュータ外科学会誌 第26回日本コンピュータ外科学会大会特集号   Vol. 19 ( 4 ) page: 343-344   2017.10

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  542. 腹腔鏡下胃切除ナビゲーションにおける変形マッチングの有限要素法モデルを検証するための動物実験

    陳 韜, 魏 国棟,何 静怡,陳 光鋒,李 鐿,師 爲禮,祁 小龍,林 雄一郎,蒋 振剛,森 健策, 李 国新

    日本コンピュータ外科学会誌 第26回日本コンピュータ外科学会大会特集号   Vol. 19 ( 4 ) page: 339-340   2017.10

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  543. 複数フレームのステレオ内視鏡画像を用いた臓器表面形状復元に関する検討

    柴田睦実,林 雄一郎,小田昌宏,三澤一成,森 健策

    日本コンピュータ外科学会誌 第26回日本コンピュータ外科学会大会特集号   Vol. 19 ( 4 ) page: 326-327   2017.10

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  544. 気管支鏡追跡における ORB-SLAM 適用に関する初期的検討

    王 成,小田昌宏,林 雄一郎,森 健策

    日本コンピュータ外科学会誌 第26回日本コンピュータ外科学会大会特集号   Vol. 19 ( 4 ) page: 324-325   2017.10

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  545. 超拡大大腸内視鏡画像を利用した病理自動診断 〜腫瘍性病変に関する分類精度解析〜

    伊東隼人,森 悠一,三澤将史,小田昌宏,工藤進英,森 健策

    日本コンピュータ外科学会誌 第26回日本コンピュータ外科学会大会特集号   Vol. 19 ( 4 ) page: 319-320   2017.10

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  546. 腹腔鏡下手術の教育支援に向けた VR 訓練システムの開発

    鈴木拓矢,道満恵介,目加田慶人,三澤一成,森 健策

    日本コンピュータ外科学会誌 第26回日本コンピュータ外科学会大会特集号   Vol. 19 ( 4 ) page: 293   2017.10

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  547. 音声認識及びジェスチャ認識による腹腔鏡下手術ナビゲーション非接触操作システムの開発

    阿部史明,道満恵介,目加田慶人,三澤一成,森 健策

    日本コンピュータ外科学会誌 第26回日本コンピュータ外科学会大会特集号   Vol. 19 ( 4 ) page: 285   2017.10

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  548. 血管芯線を用いた経時リンパ節の自動対応付け

    舘 高基,小田昌宏,中村嘉彦,三澤一成,森 健策

    日本コンピュータ外科学会誌 第26回日本コンピュータ外科学会大会特集号   Vol. 19 ( 4 ) page: 278-279   2017.10

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  549. 自動設計特徴量を用いた 3 次元腹部 CT 像における膵臓領域の位置推定

    清水南月,Holger R. Roth,小田昌宏,三澤一成,藤原道隆,森 健策

    日本コンピュータ外科学会誌 第26回日本コンピュータ外科学会大会特集号   Vol. 19 ( 4 ) page: 270-271   2017.10

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  550. 3D Fully Convolutional Networks と全連結条件付確率場による 3 次元 CT 画像からの多臓器自動抽出に関する検討

    楊 瀛,小田昌宏,Roth Holger,北坂孝幸,三澤一成, 森 健策

    日本コンピュータ外科学会誌 第26回日本コンピュータ外科学会大会特集号   Vol. 19 ( 4 ) page: 268-269   2017.10

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  551. レベルセット法を用いた腎臓皮質と髄質領域の分割

    王 成龍,小田昌宏,永山 洵,吉野 能,山本徳則,森 健策

    日本コンピュータ外科学会誌 第26回日本コンピュータ外科学会大会特集号   Vol. 19 ( 4 ) page: 266-267   2017.10

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  552. 開腹手術における 3 次元画像を用いた手術ナビゲーションシステムの臨床応用

    林 雄一郎, 三澤一成,森 健策

    日本コンピュータ外科学会誌 第26回日本コンピュータ外科学会大会特集号   Vol. 19 ( 4 ) page: 253-254   2017.10

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  553. マイクロ CT を用いた膵臓パラフィンブロック標本の解析

    進藤幸治,大内田研宙,Holger R. Roth,小田紘久,岩本千佳,小田昌宏,中村雅史,森 健策,橋爪 誠

    日本コンピュータ外科学会誌 第26回日本コンピュータ外科学会大会特集号   Vol. 19 ( 4 ) page: 244-245   2017.10

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  554. MicroCT を用いた心筋配向解析手法の取り組み 〜MRI diffusion tensor 法との比較〜'

    秋田利明,小田紘久,森 健策

    日本コンピュータ外科学会誌 第26回日本コンピュータ外科学会大会特集号   Vol. 19 ( 4 ) page: 243   2017.10

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  555. 深層学習を用いたマイクロ CT 画像からの眼球構造自動抽出 〜少量データ学習による解剖構造抽出性能の検証

    杉野貴明,Holger R. Roth,小田昌宏,小俣誠二,佐久間臣耶,新井史人,森 健策

    日本コンピュータ外科学会誌 第26回日本コンピュータ外科学会大会特集号   Vol. 19 ( 4 ) page: 241-242   2017.10

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  556. μCT 画像を用いた大変形を含む連続切片 HE 染色画像の 3 次元再構築

    長柄 快,Holger Roth,中村彰太,小田紘久,守谷享泰,小田昌宏,森 健策

    日本コンピュータ外科学会誌 第26回日本コンピュータ外科学会大会特集号   Vol. 19 ( 4 ) page: 363-364   2017.10

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  557. 色ヒストグラム特徴を用いた腹腔鏡手術映像の体外・体内シーン分類

    山田 和希, 道満 恵介, 目加田 慶人, 三澤 一成, 森 健策

    平成 29 年度日本生体医工学会東海支部大会プログラム・抄録集     page: 00   2017.10

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  558. Automatic Segmentation of Head Anatomical Structures from Sparsely-annotated Images Reviewed

    Takaaki Sugino, Holger R. Roth, Mohammad Eshghi, Masahiro Oda, Min Suk Chung, Kensaku Mori

    IEEE International Conference on Cyborg and Bionic Systems     page: 145-149   2017.10

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  559. 3D Fully Convolutional Networks と全連結条件付確率場による 3 次元 CT 画像からの多臓器自動抽出に関する検討

    楊 瀛, 小田昌宏, Roth Holger, 北坂孝幸, 三澤一成, 森 健策

    日本コンピュータ外科学会誌 第26回日本コンピュータ外科学会大会特集号   Vol. 19 ( 4 ) page: 268-269   2017.10

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  560. 畳み込みニューラルネットワークを利用した超拡大大腸内視鏡画像における腫瘍・非腫瘍の分類

    伊東 隼人, 森 悠一, 三澤 将史, 小田 昌宏, 工藤 進英, 森 健策

    電子情報通信学会技術研究報告(MI)   Vol. 117 ( 220 ) page: 17-21   2017.9

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  561. Micro-CT Guided 3D Reconstruction of Histological images

    Kai Nagara, Holger R. Roth, Shota Nakamura, Hirohisa Oda, Takayasu Moriya, Masahiro Oda, Kensaku Mori

    LNCS 10530     page: 93-101   2017.9

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  562. Motion Vector for Outlier Elimination in Feature Matching and Its Application in SLAM Based Laparoscopic Tracking

    Cheng Wang, Masahiro Oda, Yuichiro Hayashi, Kazunari Misawa, Holger Roth, Kensaku Mori

    LNCS 10550     page: 60-69   2017.9

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  563. 3D FCN Feature Driven Regression Forest-Based Pancreas Localization and Segmentation

    Masahiro Oda, Natsuki Shimizu, Holger R. Roth, Ken'ichi Karasawa, Takayuki Kitasaka, Kazunari Misawa, Michitaka Fujiwara, Daniel Rueckert, Kensaku Mori

    LNCS 10553     page: 222-230   2017.9

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  564. 3D FCN Feature Driven Regression Forest-Based Pancreas Localization and Segmentation

    Masahiro Oda, Natsuki Shimizu, Holger R. Roth, Ken'ichi Karasawa, Takayuki Kitasaka, Kazunari Misawa, Michitaka Fujiwara, Daniel Rueckert, Kensaku Mori

    LNCS 10553     page: 222-230   2017.9

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  565. Virtual 3D microscope and magnified 3D print for naked eye analyses of alveoli and alveolar duct structures by Heitzman lung specimen with micro CT

    Hiroshi Natori, Masaki Mori, Hirotsugu Takabatake, Hirotoshi Homma, ensaku Mori, Masahiro Oda, Hiroyuki Koba, Hiroki Takahashi

    ERS International congress 2017     page: Session 439   2017.9

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  566. Tracking and Segmentation of the Airways in Chest CT Using a Fully Convolutional Network

    Qier Meng, Holger R. Roth, Takayuki Kitasaka, Masahiro Oda, Junji Ueno, Kensaku Mori

    LNCS 10434     page: 198-207   2017.9

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  567. TBS: Tensor-Based Supervoxels for Unfolding the Heart

    Hirohisa Oda, Holger R. Roth, Kanwal K. Bhatia, Masahiro Oda, Takayuki Kitasaka, Toshiaki Akita, Julia A. Schnabel, Kensaku Mori

    LNCS 10433     page: 681-689   2017.9

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  568. Accuracy of diagnosing invasive colorectal cancer using computer-aided endocytoscopy Reviewed

    Kenichi Takeda, Shin-ei Kudo, Yuichi Mori, Masashi Misawa, Toyoki Kudo, Kunihiko Wakamura, Atsushi Katagiri, Toshiyuki Baba, Eiji Hidaka, Fumio Ishida, Haruhiro Inoue, Masahiro Oda, Kensaku Mori

    ENDOSCOPY   Vol. 49 ( 8 ) page: 798 - 802   2017.8

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    Background and study aims Invasive cancer carries the risk of metastasis, and therefore, the ability to distinguish between invasive cancerous lesions and less-aggressive lesions is important. We evaluated a computer-aided diagnosis system that uses ultra-high (approximately x 400) magnification endocytoscopy (EC-CAD).
    Patients and methods We generated an image database from a consecutive series of 5843 endocytoscopy images of 375 lesions. For construction of a diagnostic algorithm, 5543 endocytoscopy images from 238 lesions were randomly extracted from the database for machine learning. We applied the obtained algorithm to 200 endocytoscopy images and calculated test characteristics for the diagnosis of invasive cancer. We defined a high-confidence diagnosis as having a &gt;= 90% probability of being correct.
    Results Of the 200 test images, 188 (94.0%) were assessable with the EC-CADsystem. Sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) were 89.4%, 98.9%, 94.1%, 98.8%, and 90.1 %, respectively. High-confidence diagnosis had a sensitivity, specificity, accuracy, PPV, and NPV of 98.1%, 100%, 99.3%, 100 %, and 98.8%, respectively.
    Conclusion: EC-CADmay be a useful tool in diagnosing invasive colorectal cancer.

    DOI: 10.1055/s-0043-105486

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  569. K-means 法と Joint Unsupervised Learning による3次元医用画像の教師なしセグメンテーション

    守谷享泰, Holger R. Roth, 中村彰太, 小田紘久, 長柄快, 小田昌宏, 森健策

    第36回日本医用画像工学会大会予稿集     page: OP16-5   2017.7

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  570. Multi-atlas pancreas segmentation: Atlas selection based on vessel structure Reviewed

    Kenichi Karasawa, Masahiro Oda, Takayuki Kitasaka, Kazunari Misawa, Michitaka Fujiwara, Chengwen Chu, Guoyan Zheng, Daniel Rueckert, Kensaku Mori

    Medical Image Analysis   Vol. 39   page: 18-28   2017.7

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    DOI: 10.1016/j.media.2017.03.006

  571. 条件付き確率場による医用画像からの多臓器抽出におけるHigher Order Potential とボクセル連結構造の影響に関する考察

    楊瀛, 小田昌宏, Roth Holger, 北坂孝幸, 三澤一成, 森健策

    第36回日本医用画像工学会大会予稿集     page: OP16-4   2017.7

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  572. 3DU-Netによる3次元胸部CT像からのリンパ節検出

    小田 紘久, KanwalK.Bhatia, HolgerR.Roth, 小田 昌宏, 北坂 孝幸, 岩野 信吾, 本間 裕敏, 高畠 博嗣, 森 雅樹, 名取 博 ,JuliaA.Schnabel, 森 健策

    第36回日本医用画像工学会大会予稿集     page: OP1-6   2017.7

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  573. Torso organ segmentation in CT using fine-tuned 3D fully convolutional networks

    Holger ROTH,Ying YANG,Masahiro ODA,Hirohisa ODA, Yuichiro HAYASHI,Natsuki SHIMIZU,Takayuki KITASAKA,Michitaka FUJIWARA,Kazunari MISAWA,Kensaku MORI

        page: OP1-8   2017.7

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  574. Improvement on Robustness of ORB-SLAM Based Surgical Navigation System by Building Submap

    王成 , 小田昌宏, 林雄一郎, 三澤一成, 森健策

    第36回日本医用画像工学会大会予稿集     page: OP2-6   2017.7

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  575. ステレオ内視鏡画像からの臓器形状復元手法における複数フレームの利用に関する初期的検討

    柴田 睦実, 林 雄一郎, 小田 昌宏, 三澤 一成, 森 健策

    第36回日本医用画像工学会大会予稿集     page: OP2-8   2017.7

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  576. 機械学習を用いた腹部動脈血管名自動命名における肝動脈分岐情報利用方法に関する一考察

    鉄村 悠介, 張 暁楠, Holger Roth, 林 雄一郎, 小田 昌宏, 三澤 一成, 森 健策

    第36回日本医用画像工学会大会予稿集     page: OP6-1   2017.7

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  577. A Study on Fine Blood Vessel Segmentation Using Fully-connected Conditional Random Field

        page: OP11-2   2017.7

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  578. CT 像から抽出した腹部動脈領域におけるCNN を用いた過検出削減でのパッチ画像生成手法の検討

    小田 昌宏, 山本 徳則, 吉野 能, 森 健策

    第36回日本医用画像工学会大会予稿集     page: OP11-7   2017.7

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  579. マイクロ CT 画像情報を利用した特徴点対応付けに基づく顕微鏡画像の 3 次元再構築

    長柄 快, Holger R. ROTH, 中村 彰太, 小田 紘久, 守谷 享泰, 小田 昌宏, 森 健策

    第36回日本医用画像工学会大会予稿集     page: OP14-1   2017.7

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  580. 血管情報を用いた経時リンパ節の自動対応付け手法に関する研究

    舘 高基, 小田 昌宏, 中村 嘉彦, 寶珠山 裕, 三澤 一成, 森 健策

    第36回日本医用画像工学会大会予稿集     page: OP15-4   2017.7

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  581. A Study on Fine Blood Vessel Segmentation Using Fully-connected Conditional Random Field

        page: OP11-2   2017.7

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  582. 3DU-Netによる3次元胸部CT像からのリンパ節検出

    小田 紘久, KanwalK.Bhatia, HolgerR.Roth, 小田 昌宏, 北坂 孝幸, 岩野 信吾, 本間 裕敏, 高畠 博嗣, 森 雅樹, 名取 博, JuliaA.Schnabel, 森 健策

    第36回日本医用画像工学会大会予稿集     page: OP1-6   2017.7

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  583. Feature-based registration of micro CT volumes

    Kai Nagara, Shota Nakamura, Hoiger R. Roth, Masahiro Oda, Hirotoshi Homma, Hirotsugu Takabatake, Masaki Mori, Hiroshi Natori, Kesaku Mori

    International Journal of Computer Assisted Radiology and Surgery   Vol. 12   page: S201-S203   2017.6

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  584. Multi-organ segmentation in abdominal CT using 3D fully convolutional networks

    Holger R. Roth, Masahiro Oda, Yuichiro Hayashi, Hirohisa Oda, Kensaku Mori

    International Journal of Computer Assisted Radiology and Surgery   Vol. 12   page: S55-S57   2017.6

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  585. Feature-based registration of micro CT volumes

    Kai Nagara, Shota Nakamura, Hoiger R. Roth, Masahiro Oda, Hirotoshi Homma, Hirotsugu Takabatake, Masaki Mori, Hiroshi Natori, Kesaku Mori

    International Journal of Computer Assisted Radiology and Surgery   Vol. 12   page: S201-S203   2017.6

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  586. Multi-organ segmentation in abdominal CT using 3D fully convolutional networks

    Holger R. Roth, Masahiro Oda, Yuichiro Hayashi, Hirohisa Oda, Kensaku Mori

    International Journal of Computer Assisted Radiology and Surgery   Vol. 12   page: S55-S57   2017.6

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  587. Endocytoscope image classification using deep convolutional neural networks

    Masahiro Oda, Yutaka Hoshiyama, Masashi Misawa, Yuichi Mori, Kenichi Takeda, Sin-ei Kudo, Kensaku Mori

    International Journal of Computer Assisted Radiology and Surgery   Vol. 12   page: S147-S148   2017.6

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  588. False positive reduction of abdominal artery segmentation from CT volumes based on deep convolutional neural networks

    Masahiro Oda, Tokunori Yamamoto, Yasushi Yoshino, Kensaku Mori

    International Journal of Computer Assisted Radiology and Surgery   Vol. 12   page: S27-S29   2017.6

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  589. Endocytoscope image classification using deep convolutional neural networks

    Masahiro Oda, Yutaka Hoshiyama, Masashi Misawa, Yuichi Mori, Kenichi Takeda, Sin-ei Kudo, Kensaku Mori

    International Journal of Computer Assisted Radiology and Surgery   Vol. 12   page: S147-S148   2017.6

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  590. False positive reduction of abdominal artery segmentation from CT volumes based on deep convolutional neural networks

    Masahiro Oda, Tokunori Yamamoto, Yasushi Yoshino, Kensaku Mori

    International Journal of Computer Assisted Radiology and Surgery   Vol. 12   page: S27-S29   2017.6

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  591. Automatic Anatomical Labeling of Arteries and Veins Using Conditional Random Fields

    Takayuki Kitasaka, Mitsuru Kagajo, Yukitaka Nimura, Yuichiro Hayashi, Masahiro Oda, Kazunari Misawa, Kensaku Mori

    8th International Conference on Information Processing in Computer-Assisted Interventions (IPCAI 2017)     page: -   2017.6

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  592. Automatic Anatomical Labeling of Arteries and Veins Using Conditional Random Fields

    Takayuki Kitasaka, Mitsuru Kagajo, Yukitaka Nimura, Yuichiro Hayashi, Masahiro Oda, Kazunari Misawa, Kensaku Mori

    8th International Conference on Information Processing in Computer-Assisted Interventions (IPCAI 2017)     page: -   2017.6

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  593. Automatic anatomical labeling of arteries and veins using conditional random fields Reviewed

    Takayuki Kitasaka, Mitsuru Kagajo, Yukitaka Nimura, Yuichiro Hayashi, Masahiro Oda, Kazunari Misawa, Kensaku Mori

    INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY   Vol. 12 ( 6 ) page: 1041 - 1048   2017.6

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    For safe and reliable laparoscopic surgery, it is important to determine individual differences of blood vessels such as the position, shape, and branching structures. Consequently, a computer-assisted laparoscopy that displays blood vessel structures with anatomical labels would be extremely beneficial. This paper details an automated anatomical labeling method for abdominal arteries and veins extracted from 3D CT volumes.
    The proposed method represents a blood vessel tree as a probabilistic graphical model by conditional random fields (CRFs). An adaptive gradient algorithm is adopted for structure learning. The anatomical labeling of blood vessel branches is performed by maximum a posteriori estimation.
    We applied the proposed method to 50 cases of arterial and portal phase abdominal X-ray CT volumes. The experimental results showed that the F-measure of the proposed method for abdominal arteries and veins was 94.4 and 86.9%, respectively.
    We developed an automated anatomical labeling method to annotate each blood vessel branches of abdominal arteries and veins using CRF. The proposed method outperformed a state-of-the-art method.

    DOI: 10.1007/s11548-017-1549-x

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  594. Accuracy of computer-aided diagnosis based on narrow-band imaging endocytoscopy for diagnosing colorectal lesions: comparison with experts Reviewed

    Masashi Misawa, Shin-ei Kudo, Yuichi Mori, Kenichi Takeda, Yasuharu Maeda, Shinichi Kataoka, Hiroki Nakamura, Toyoki Kudo, Kunihiko Wakamura, Takemasa Hayashi, Atsushi Katagiri, Toshiyuki Baba, Fumio Ishida, Haruhiro Inoue, Yukitaka Nimura, Msahiro Oda, Kensaku Mori

    INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY   Vol. 12 ( 5 ) page: 757 - 766   2017.6

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    Real-time characterization of colorectal lesions during colonoscopy is important for reducing medical costs, given that the need for a pathological diagnosis can be omitted if the accuracy of the diagnostic modality is sufficiently high. However, it is sometimes difficult for community-based gastroenterologists to achieve the required level of diagnostic accuracy. In this regard, we developed a computer-aided diagnosis (CAD) system based on endocytoscopy (EC) to evaluate cellular, glandular, and vessel structure atypia in vivo. The purpose of this study was to compare the diagnostic ability and efficacy of this CAD system with the performances of human expert and trainee endoscopists.
    We developed a CAD system based on EC with narrow-band imaging that allowed microvascular evaluation without dye (ECV-CAD). The CAD algorithm was programmed based on texture analysis and provided a two-class diagnosis of neoplastic or non-neoplastic, with probabilities. We validated the diagnostic ability of the ECV-CAD system using 173 randomly selected EC images (49 non-neoplasms, 124 neoplasms). The images were evaluated by the CAD and by four expert endoscopists and three trainees. The diagnostic accuracies for distinguishing between neoplasms and non-neoplasms were calculated.
    ECV-CAD had higher overall diagnostic accuracy than trainees (87.8 vs 63.4%; ), but similar to experts (87.8 vs 84.2%; ). With regard to high-confidence cases, the overall accuracy of ECV-CAD was also higher than trainees (93.5 vs 71.7%; ) and comparable to experts (93.5 vs 90.8%; ).
    ECV-CAD showed better diagnostic accuracy than trainee endoscopists and was comparable to that of experts. ECV-CAD could thus be a powerful decision-making tool for less-experienced endoscopists.

    DOI: 10.1007/s11548-017-1542-4

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  595. 機械学習を用いた内視鏡画像自動診断

    第56回 日本生体医工学会大会 プログラム・抄録集     page: 344   2017.5

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  596. 3Dプリンタの医療応用

    森 健策

    医用画像情報学会雑誌   Vol. 34 ( 1 ) page: 1-6   2017.4

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  597. 3Dプリンタの医療応用

    森 健策

    医用画像情報学会雑誌   Vol. 34 ( 1 ) page: 1-6   2017.4

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  598. Development of an automatic producing cholangiography procedure reconstructed from portal phase multidetector-row computed tomography images: Preliminary experience Reviewed

    Tomoaki Hirose, Tsuyoshi Igami, Kusuto Koga, Yuichiro Hayashi, Tomoki Ebata, Yukihiro, Yokoyama, Gen Sugawara, Takashi Mizuno, Junpei Yamaguchi, Kensaku Mori, Masato Nagino

    Surgery Today   Vol. 47 ( 3 ) page: 365-374   2017.3

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    DOI: 10.1007/s00595-016-1394-5

  599. Supervoxel classification forests for estimating pairwise image correspondences Reviewed

    Fahdi Kanavati, Tong Tong, Kazunari Misawa, Michitaka Fujiwara, Kensaku Mori, Daniel Rueckert, Ben Glocker

    Pattern Recognition   Vol. 63   page: 561-569   2017.3

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    DOI: 10.1016/j.patcog.2016.09.026

  600. An improved method for pancreas segmentation using SLIC superpixels and interactive region merging

    Liyuan Zhang, Huamin Yang, Weili Shi, Yu Miao, Fei He, Wei He, Yanfang Li, Fei Yan, Huimao Zhang, Kensaku Mori, Zhengang Jiang

    Proceedings of SPIE   Vol. 10134   page: 101343H-1-101343H-12   2017.2

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    DOI: doi:10.1117/12.2254366

  601. Hessian-assisted supervoxel: structure-oriented voxel clustering and application to mediastinal lymph node detection from CT volumes

    Hirohisa Oda, Kanwal Bhatia, Masahiro Oda, Takayuki Kitasaka, Shingo Iwano, Hirotoshi Homma, Hirotsugu Takabatake, Masaki Mori, Hiroshi Natori, Julia A. Schnabel, Kensaku Mori

    Proceedings of SPIE   Vol. 10134   page: 101341D-1-101341D-12   2017.2

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    DOI: 10.1117/12.2254782

  602. Computer-aided diagnosis of mammographic masses using geometric verification-based image retrieval

    Qingliang Li, Weili SHI, Huamin Yang, Huimao Zhang, Tao CHEN, Kensaku Mori, Guoxin LI, Zhengang Jiang

    Proceedings of SPIE   Vol. 10134   page: 101342W-1-101342W-8   2017.2

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    DOI: doi:10.1117/12.2255799

  603. Extracellular matrix directions estimation of the heart on microfocus X-ray CT volumes

    Hirohisa Oda, Masahiro Oda, Takayuki Kitasaka, Toshiaki Akita, Kensaku Mori

    Proceedings of SPIE   Vol. 10137   page: 101370M-1-101370M-9   2017.2

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    DOI: 10.1117/12.2254949

  604. Automatic segmentation of airway tree based on local intensity filter and machine learning technique in 3D chest CT volume Reviewed

    Qier Meng, Takayuki Kitasaka, Yukitaka Nimura, Masahiro Oda, Junji Ueno, Kensaku Mori

    INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY   Vol. 12 ( 2 ) page: 245 - 261   2017.2

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    Airway segmentation plays an important role in analyzing chest computed tomography (CT) volumes for computerized lung cancer detection, emphysema diagnosis and pre- and intra-operative bronchoscope navigation. However, obtaining a complete 3D airway tree structure from a CT volume is quite a challenging task. Several researchers have proposed automated airway segmentation algorithms basically based on region growing and machine learning techniques. However, these methods fail to detect the peripheral bronchial branches, which results in a large amount of leakage. This paper presents a novel approach for more accurate extraction of the complex airway tree.
    This proposed segmentation method is composed of three steps. First, Hessian analysis is utilized to enhance the tube-like structure in CT volumes; then, an adaptive multiscale cavity enhancement filter is employed to detect the cavity-like structure with different radii. In the second step, support vector machine learning will be utilized to remove the false positive (FP) regions from the result obtained in the previous step. Finally, the graph-cut algorithm is used to refine the candidate voxels to form an integrated airway tree.
    A test dataset including 50 standard-dose chest CT volumes was used for evaluating our proposed method. The average extraction rate was about 79.1 % with the significantly decreased FP rate.
    A new method of airway segmentation based on local intensity structure and machine learning technique was developed. The method was shown to be feasible for airway segmentation in a computer-aided diagnosis system for a lung and bronchoscope guidance system.

    DOI: 10.1007/s11548-016-1492-2

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  605. Extraction of membrane structure in eyeball from MR volumes

    Masahiro Oda, Kin Taichi, Kensaku Mori

    Proceedings of SPIE   Vol. 10137   page: 101371S-1-101371S-6   2017.2

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    DOI: 10.1117/12.2254095

  606. A study on surgical field retrieval for intelligent laparotomy video archive system

    Takayuki Kitasaka, Yuki Kondo, Yuri Kimura, Yuki Takanashi, Hiroaki Sawano, Yasuhito Suenaga, Kazunari Misawa, and Kensaku Mori

    International Forum on Medical Imaging in Asia (IFMIA)     page: 327-328   2017.1

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  607. GPU Implementation of SLIC Supervoxel Oversegmentation

    Hirohisa Oda, Kanwal K. Bhatia, Masahiro Oda, Takayuki Kitasaka, Shingo Iwano, Julia A. Schnabel, and Kensaku Mori

    International Forum on Medical Imaging in Asia (IFMIA)     page: 266-268   2017.1

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  608. A study on surgical field retrieval for intelligent laparotomy video archive system

    Takayuki Kitasaka, Yuki Kondo, Yuri Kimura, Yuki Takanashi, Hiroaki Sawano, Yasuhito Suenaga, Kazunari Misawa, Kensaku Mori

    International Forum on Medical Imaging in Asia (IFMIA)     page: 327-328   2017.1

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  609. Airway segmentation from 3D chest CT volumes based on volume of interest using gradient vector flow

    Qier Meng, Takayuki Kitasaka, Masahiro Oda, and Kensaku Mori

    International Forum on Medical Imaging in Asia (IFMIA)     page: 192-195   2017.1

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  610. Multi-scale Image Fusion Between Pre-operative Clinical CT and X-ray microtomography of Lung Pathology

    Holger Roth, Kai Nagara, Hirohisa Oda, Masahiro Oda, Tomoshi Sugiyama, Shota Nakamura, and Kensaku Mori

    International Forum on Medical Imaging in Asia (IFMIA)     page: 54-56   2017.1

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  611. Airway segmentation from 3D chest CT volumes based on volume of interest using gradient vector flow

    Qier Meng, Takayuki Kitasaka, Masahiro Oda, Kensaku Mori

    International Forum on Medical Imaging in Asia (IFMIA)     page: 192-195   2017.1

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  612. Structured Random Forestを用いた3次元腹部CT像からのリンパ節自動検出

    寳珠山 裕, Holger Roth, 小田 昌宏, 中村 嘉彦, 三澤 一成, 藤原 道隆, 森 健策

    電子情報通信学会技術研究報告(MI)   Vol. 116 ( 393 ) page: 23-28   2017.1

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  613. MISTライブラリのためのGPUプログラミング

    小田 紘久, 小田 昌宏, 北坂 孝幸, 森 健策

    電子情報通信学会技術研究報告(MI)   Vol. 116 ( 393 ) page: 133-136   2017.1

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  614. Deep-Learning-Based Segmentation for the Head Sectioned Images of the Visible Korean Project

    Mohammad Eshghi, Holger R. Roth, Hirohisa Oda, Masahiro Oda, Min Suk Chung, Kensaku Mori

    MI2016-119   Vol. 116 ( 393 ) page: 191-194   2017.1

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  615. Influence of Voxel-Connection Structure in Organ Segmentation Based on Conditional Random Field

    Ying Yang, Masahiro Oda, Kazunari Misawa, Daniel Rueckert, Kensaku Mori

    MI2016-112   Vol. 116 ( 393 ) page: 157-162   2017.1

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  616. MICCAI 2016参加報告

    小田 昌宏, 宮内 翔子, 諸岡 健一, 周 向栄, 増谷 佳孝, 中口 俊哉, 井宮 淳, 森 健策

    電子情報通信学会技術研究報告(MI)   Vol. 116 ( 393 ) page: 185-190   2017.1

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  617. Robust colonoscope tracking method for colon deformations utilizing coarse-to-fine correspondence findings Reviewed

    Masahiro Oda, Hiroaki Kondo, Takayuki Kitasaka, Kazuhiro Furukawa, Ryoji Miyahara, Yoshiki Hirooka, Hidemi Goto, Nassir Navab, Kensaku Mori

    INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY   Vol. 12 ( 1 ) page: 39 - 50   2017.1

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    Polyps found during CT colonography can be removed by colonoscopic polypectomy. A colonoscope navigation system that navigates a physician to polyp positions while performing the colonoscopic polypectomy is required. Colonoscope tracking methods are essential for implementing colonoscope navigation systems. Previous colonoscope tracking methods have failed when the colon deforms during colonoscope insertions. This paper proposes a colonoscope tracking method that is robust against colon deformations.
    The proposed method generates a colon centerline from a CT volume and a curved line representing the colonoscope shape (colonoscope line) by using electromagnetic sensors. We find correspondences between points on a deformed colon centerline and colonoscope line by a landmark-based coarse correspondence finding and a length-based fine correspondence finding processes. Even if the coarse correspondence finding process fails to find some correspondences, which occurs with colon deformations, the fine correspondence finding process is able to find correct correspondences by using previously recorded line lengths.
    Experimental results using a colon phantom showed that the proposed method finds the colonoscope tip position with tracking errors smaller than 50 mm in most trials. A physician who specializes in gastroenterology commented that tracking errors smaller than 50 mm are acceptable. This is because polyps are observable from the colonoscope camera when positions of the colonoscope tip and polyps are closer than 50 mm.
    We developed a colonoscope tracking method that is robust against deformations of the colon. Because the process was designed to consider colon deformations, the proposed method can track the colonoscope t