Faculty Profiles - MORI, Kensaku

Papers - MORI, Kensaku

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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

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

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

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

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

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

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

Precise estimation of renal vascular dominant regions using spatially aware fully convolutional networks, tensor-cut and Voronoi diagrams Open Access

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

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

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

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

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

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

Artificial intelligence and upper gastrointestinal endoscopy: Current status and future perspective. International journal Open Access

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

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

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 Reviewed Open Access

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