論文 - 森　健策

Label cleaning and propagation for improved segmentation performance using fully convolutional networks 査読有り

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年3月

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

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

Predicting Violence Rating Based on Pairwise Comparison. 査読有り

Ying JI, Yu WANG, Jien KATO, Kensaku MORI

IEICE Transactions on Information and Systems   E103.D 巻 ( 12 ) 頁： 2578 - 2589   2020年12月

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担当区分：最終著者   記述言語：英語   掲載種別：研究論文（学術雑誌）  

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

Anatomical attention can help to segment the dilated pancreatic duct in abdominal CT. 招待有り 査読有り

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

International journal of computer assisted radiology and surgery   19 巻 ( 4 ) 頁： 655 - 664   2024年4月

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担当区分：責任著者   記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：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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Artificial intelligence-based diagnostic imaging system with virtual enteroscopy and virtual unfolded views to evaluate small bowel lesions in Crohn's disease. 招待有り 査読有り

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

Revista espanola de enfermedades digestivas     2024年3月

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担当区分：責任著者   記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.17235/reed.2024.10405/2024

PubMed

Development of real-time navigation system for laparoscopic hepatectomy using magnetic micro sensor. 招待有り 査読有り

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     頁： 1 - 11   2024年1月

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担当区分：責任著者   記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：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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Deep learning model for extensive smartphone-based diagnosis and triage of cataracts and multiple corneal diseases. 招待有り 査読有り

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年1月

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担当区分：責任著者   記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：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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Endoscope Automation Framework with Hierarchical Control and Interactive Perception for Multi-Tool Tracking in Minimally Invasive Surgery. 招待有り 査読有り

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

Sensors (Basel, Switzerland)   23 巻 ( 24 )   2023年12月

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担当区分：責任著者   記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：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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Artificial intelligence for evaluating the risk of gastric cancer: reliable detection and scoring of intestinal metaplasia with deep learning algorithms. 招待有り 査読有り

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

Gastrointestinal endoscopy   98 巻 ( 6 ) 頁： 925 - 933.e1   2023年12月

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担当区分：責任著者   記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：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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Deep learning-based prediction model for postoperative complications of cervical posterior longitudinal ligament ossification. 招待有り 査読有り

Ito S, Nakashima H, Yoshii T, Egawa S, Sakai K, Kusano K, Tsutui S, Hirai T, Matsukura Y, Wada K, Katsumi K, Koda M, Kimura A, Furuya T, Maki S, Nagoshi N, Nishida N, Nagamoto Y, Oshima Y, Ando K, Takahata M, Mori K, Nakajima H, Murata K, Miyagi M, Kaito T, Yamada K, Banno T, Kato S, Ohba T, Inami S, Fujibayashi S, Katoh H, Kanno H, Oda M, Mori K, Taneichi H, Kawaguchi Y, Takeshita K, Matsumoto M, Yamazaki M, Okawa A, Imagama S

European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society   32 巻 ( 11 ) 頁： 3797 - 3806   2023年11月

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担当区分：責任著者   記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：European Spine Journal  

Purpose: Postoperative complication prediction helps surgeons to inform and manage patient expectations. Deep learning, a model that finds patterns in large samples of data, outperform traditional statistical methods in making predictions. This study aimed to create a deep learning-based model (DLM) to predict postoperative complications in patients with cervical ossification of the posterior longitudinal ligament (OPLL). Methods: This prospective multicenter study was conducted by the 28 institutions, and 478 patients were included in the analysis. Deep learning was used to create two predictive models of the overall postoperative complications and neurological complications, one of the major complications. These models were constructed by learning the patient's preoperative background, clinical symptoms, surgical procedures, and imaging findings. These logistic regression models were also created, and these accuracies were compared with those of the DLM. Results: Overall complications were observed in 127 cases (26.6%). The accuracy of the DLM was 74.6 ± 3.7% for predicting the overall occurrence of complications, which was comparable to that of the logistic regression (74.1%). Neurological complications were observed in 48 cases (10.0%), and the accuracy of the DLM was 91.7 ± 3.5%, which was higher than that of the logistic regression (90.1%). Conclusion: A new algorithm using deep learning was able to predict complications after cervical OPLL surgery. This model was well calibrated, with prediction accuracy comparable to that of regression models. The accuracy remained high even for predicting only neurological complications, for which the case number is limited compared to conventional statistical methods.

DOI： 10.1007/s00586-023-07562-2

DOI： 10.1007/s00586-023-07562-2

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Social Relation Atmosphere Recognition with Relevant Visual Concepts 招待有り

Ying J.I., Wang Y., Mori K., Kato J.

IEICE Transactions on Information and Systems   E106.D 巻 ( 10 ) 頁： 1638 - 1649   2023年10月

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担当区分：責任著者   記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：IEICE Transactions on Information and Systems  

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.

DOI： 10.1587/transinf.2023PCP0008

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CiNii Research

Automated Detection and Diagnosis of Spinal Schwannomas and Meningiomas Using Deep Learning and Magnetic Resonance Imaging. 招待有り 査読有り

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

Journal of clinical medicine   12 巻 ( 15 )   2023年8月

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担当区分：責任著者   記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：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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Activities of National Institute of Informatics in Japan 招待有り 査読有り

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

Communications of the ACM   66 巻 ( 7 ) 頁： 58 - 63   2023年6月

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担当区分：責任著者   記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：Communications of the ACM  

DOI： 10.1145/3589736

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Speedometer for withdrawal time monitoring during colonoscopy: a clinical implementation trial. 招待有り 査読有り

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   58 巻 ( 6 ) 頁： 664 - 670   2023年6月

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担当区分：責任著者   記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：Scandinavian Journal of Gastroenterology  

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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Correction to: Gaussian affinity and GIoU-based loss for perforation detection and localization from colonoscopy videos. 招待有り 査読有り

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   18 巻 ( 5 ) 頁： 807   2023年5月

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担当区分：責任著者   記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：International Journal of Computer Assisted Radiology and Surgery  

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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Gaussian affinity and GIoU-based loss for perforation detection and localization from colonoscopy videos. 招待有り 査読有り

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   18 巻 ( 5 ) 頁： 795 - 805   2023年5月

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担当区分：責任著者   記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：International Journal of Computer Assisted Radiology and Surgery  

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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Class-wise confidence-aware active learning for laparoscopic images segmentation 招待有り 査読有り

Qiu, J; Hayashi, Y; Oda, M; Kitasaka, T; Mori, K

INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY   18 巻 ( 3 ) 頁： 473 - 482   2023年3月

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担当区分：責任著者   記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：International Journal of Computer Assisted Radiology and Surgery  

Purpose: Segmentation tasks are important for computer-assisted surgery systems as they provide the shapes of organs and the locations of instruments. What prevents the most powerful segmentation approaches from becoming practical applications is the requirement for annotated data. Active learning provides strategies to dynamically select the most informative samples to reduce the annotation workload. However, most previous active learning literature has failed to select the frames that containing low-appearing frequency classes, even though the existence of these classes is common in laparoscopic videos, resulting in poor performance in segmentation tasks. Furthermore, few previous works have explored the unselected data to improve active learning. Therefore, in this work, we focus on these classes to improve the segmentation performance. Methods: We propose a class-wise confidence bank that stores and updates the confidence scores for each class and a new acquisition function based on a confidence bank. We apply confidence scores to explore an unlabeled dataset by combining it with a class-wise data mixture method to exploit unlabeled datasets without any annotation. Results: We validated our proposal on two open-source datasets, CholecSeg8k and RobSeg2017, and observed that its performance surpassed previous active learning studies with about 10 % improvement on CholecSeg8k, especially for classes with a low-appearing frequency. For robSeg2017, we conducted experiments with a small and large annotation budgets to validate situation that shows the effectiveness of our proposal. Conclusions: We presented a class-wise confidence score to improve the acquisition function for active learning and explored unlabeled data with our proposed class-wise confidence score, which results in a large improvement over the compared methods. The experiments also showed that our proposal improved the segmentation performance for classes with a low-appearing frequency.

DOI： 10.1007/s11548-022-02773-2

Web of Science

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A skeleton context-aware 3D fully convolutional network for abdominal artery segmentation 招待有り 査読有り

Zhu, RY; Oda, M; Hayashi, Y; Kitasaka, T; Misawa, K; Fujiwara, M; Mori, K

INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY   18 巻 ( 3 ) 頁： 461 - 472   2023年3月

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担当区分：責任著者   記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：International Journal of Computer Assisted Radiology and Surgery  

Purpose: This paper aims to propose a deep learning-based method for abdominal artery segmentation. Blood vessel structure information is essential to diagnosis and treatment. Accurate blood vessel segmentation is critical to preoperative planning. Although deep learning-based methods perform well on large organs, segmenting small organs such as blood vessels is challenging due to complicated branching structures and positions. We propose a 3D deep learning network from a skeleton context-aware perspective to improve segmentation accuracy. In addition, we propose a novel 3D patch generation method which could strengthen the structural diversity of a training data set. Method: The proposed method segments abdominal arteries from an abdominal computed tomography (CT) volume using a 3D fully convolutional network (FCN). We add two auxiliary tasks to the network to extract the skeleton context of abdominal arteries. In addition, our skeleton-based patch generation (SBPG) method further enables the FCN to segment small arteries. SBPG generates a 3D patch from a CT volume by leveraging artery skeleton information. These methods improve the segmentation accuracies of small arteries. Results: We used 20 cases of abdominal CT volumes to evaluate the proposed method. The experimental results showed that our method outperformed previous segmentation accuracies. The averaged precision rate, recall rate, and F-measure were 95.5%, 91.0%, and 93.2%, respectively. Compared to a baseline method, our method improved 1.5% the averaged recall rate and 0.7% the averaged F-measure. Conclusions: We present a skeleton context-aware 3D FCN to segment abdominal arteries from an abdominal CT volume. In addition, we propose a 3D patch generation method. Our fully automated method segmented most of the abdominal artery regions. The method produced competitive segmentation performance compared to previous methods.

DOI： 10.1007/s11548-022-02767-0

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Deep Learning-Based Seminal Vesicle and Vas Deferens Recognition in the Posterior Approach of Robot-Assisted Radical Prostatectomy. 招待有り 査読有り

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   173 巻   頁： 98 - 103   2023年3月

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担当区分：責任著者   記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：Urology  

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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PubMed

Database-driven patient-specific registration error compensation method for image-guided laparoscopic surgery. 招待有り 査読有り

Hayashi Y, Misawa K, Mori K

International journal of computer assisted radiology and surgery   18 巻 ( 1 ) 頁： 63 - 69   2023年1月

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担当区分：責任著者   記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：International Journal of Computer Assisted Radiology and Surgery  

Purpose: A surgical navigation system helps surgeons understand anatomical structures in the operative field during surgery. Patient-to-image registration, which aligns coordinate systems between the CT volume and a positional tracker, is vital for accurate surgical navigation. Although a point-based rigid registration method using fiducials on the body surface is often utilized for laparoscopic surgery navigation, precise registration is difficult due to such factors as soft tissue deformation. We propose a method that compensates a transformation matrix computed using fiducials on the body surface based on the analysis of positional information in the database. Methods: We built our database by measuring the positional information of the fiducials and the guidance targets in both the CT volume and positional tracker coordinate systems through previous surgeries. We computed two transformation matrices: using only the fiducials and using only the guidance targets in all the data in the database. We calculated the differences between the two transformation matrices in each piece of data. The compensation transformation matrix was computed by averaging these difference matrices. In this step, we selected the data from the database based on the similarity of the fiducials and the configuration of the guidance targets. Results: We evaluated our proposed method using 20 pieces of data acquired during laparoscopic gastrectomy for gastric cancer. The locations of blood vessels were used as guidance targets for computing target registration error. The mean target registration errors significantly decreased from 33.0 to 17.1 mm before and after the compensation. Conclusion: This paper described a registration error compensation method using a database for image-guided laparoscopic surgery. Since our proposed method reduced registration error without additional intraoperative measurements during surgery, it increases the accuracy of surgical navigation for laparoscopic surgery.

DOI： 10.1007/s11548-022-02804-y

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PubMed

Computer-Aided Size Estimation of Colorectal Polyps 招待有り 査読有り

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

Techniques and Innovations in Gastrointestinal Endoscopy   25 巻 ( 2 ) 頁： 186 - 188   2023年1月

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担当区分：責任著者   記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：Techniques and Innovations in Gastrointestinal Endoscopy  

DOI： 10.1016/j.tige.2022.11.004

Scopus