Faculty Profiles - MORI, Kensaku

Papers - MORI, Kensaku

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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

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

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

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

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

Minimally invasive therapy & allied technologies : MITAT : official journal of the Society for Minimally Invasive Therapy Vol. 29 ( 4 ) page： 210 - 216 2020.7

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

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

Web of Science

Scopus

PubMed

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 - + 2020.7

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

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

Web of Science

Scopus

PubMed

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

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

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

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

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

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

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

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

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

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

Cardiac fiber tracking on super high-resolution CT images: a comparative study. Invited International journal Open Access

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

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

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

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

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

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

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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Papers - MORI, Kensaku