研究者詳細 - 森　健策

論文 - 森　健策

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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 LNCS 11769 巻頁： 348-356 2019年10月

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 LNCS 11827 巻頁： 90-99 2019年10月

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 LNCS 11840 巻頁： 104-114 2019年10月

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 ) 頁： 6-8 2019年10月

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 77 巻 ( 10642 ) 頁： 1-13 2019年10月

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 頁： 240-248 2019年10月

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 頁： 348-356 2019年10月

Intelligent Image Synthesis to Attack a segmentation CNN Using Adversarial Learning

Liang Chen, Paul Bentley, Kensaku Mori, Kazunari Misawa, Michitaka Fujiwara, Daniel Rueckert,

LNCS11827 頁： 90-99 2019年10月

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 頁： 104-114 2019年10月

Artificial Intelligence-assisted System Improves Endoscopic Identification of Colorectal Neoplasms 査読有り国際誌

Kudo S.e., Misawa M., Mori Y., Hotta K., Ohtsuka K., Ikematsu H., Saito Y., Takeda K., Nakamura H., Ichimasa K., Ishigaki T., Toyoshima N., Kudo T., Hayashi T., Wakamura K., Baba T., Ishida F., Inoue H., Itoh H., Oda M., Mori K.

Clinical Gastroenterology and Hepatology 18 巻 ( 8 ) 頁： 1874 - 1881 2019年9月

記述言語：英語掲載種別：研究論文（学術雑誌）出版者・発行元：Clinical Gastroenterology and Hepatology

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

Scopus

PubMed

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・抄録集頁： 11 2019年7月

Artificial intelligence and upper gastrointestinal endoscopy: Current status and future perspective 国際誌

Mori Yuichi, Kudo Shin-ei, Mohmed Hussein E. N., Misawa Masashi, Ogata Noriyuki, Itoh Hayato, Oda Masahiro, Mori Kensaku

DIGESTIVE ENDOSCOPY 31 巻 ( 4 ) 頁： 378 - 388 2019年7月

記述言語：日本語掲載種別：研究論文（学術雑誌）出版者・発行元：Digestive Endoscopy

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

Artificial intelligence and upper gastrointestinal endoscopy: current status and future perspective 査読有り

Yuichi Mori, Shinei Kudo, Hussein Ebaid Naeem Mohmed, Masashi Misawa, Noriyuki Ogata, Hayato Itoh, Masahiro Oda, Kensaku Mori

Digestive endoscopy 34 巻 ( 4 ) 頁： 378-388 2019年7月