Faculty Profiles - MORI, Kensaku

Papers - MORI, Kensaku

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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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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Papers - MORI, Kensaku