Faculty Profiles - MORI, Kensaku

Papers - MORI, Kensaku

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超拡大内視鏡画像における腫瘍性ポリープ分類に向けたグラスマン距離に基づく特徴選択法

伊東隼人, 森　悠一, 三澤将史, 小田昌宏, 工藤進英, 森　健策

電子情報通信学会技術研究報告(MI) Vol. 117 ( 518 ) page： 51-56 2018.3

開腹手術映像における遮蔽物除去手法の改善 FFDによる位置合わせ精度の評価

北坂孝幸, 奥田透生, 佐藤　準, 豊田誠仁, 澤野弘明, 末永康仁, 三澤一成, 森健策

電子情報通信学会技術研究報告(MI) Vol. 117 ( 518 ) page： 31-32 2018.3

Artificial intelligence may help in predicting the need for additional surgery after endoscopic resection of T1 colorectal cancer. Reviewed International journal

Katsuro Ichimasa, Shin-Ei Kudo, Yuichi Mori, Masashi Misawa, Shingo Matsudaira, Yuta Kouyama, Toshiyuki Baba, Eiji Hidaka, Kunihiko Wakamura, Takemasa Hayashi, Toyoki Kudo, Tomoyuki Ishigaki, Yusuke Yagawa, Hiroki Nakamura, Kenichi Takeda, Amyn Haji, Shigeharu Hamatani, Kensaku Mori, Fumio Ishida, Hideyuki Miyachi

Endoscopy Vol. 50 ( 3 ) page： 230 - 240 2018.3

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

BACKGROUND AND STUDY AIMS: Decisions concerning additional surgery after endoscopic resection of T1 colorectal cancer (CRC) are difficult because preoperative prediction of lymph node metastasis (LNM) is problematic. We investigated whether artificial intelligence can predict LNM presence, thus minimizing the need for additional surgery. PATIENTS AND METHODS: Data on 690 consecutive patients with T1 CRCs that were surgically resected in 2001 - 2016 were retrospectively analyzed. We divided patients into two groups according to date: data from 590 patients were used for machine learning for the artificial intelligence model, and the remaining 100 patients were included for model validation. The artificial intelligence model analyzed 45 clinicopathological factors and then predicted positivity or negativity for LNM. Operative specimens were used as the gold standard for the presence of LNM. The artificial intelligence model was validated by calculating the sensitivity, specificity, and accuracy for predicting LNM, and comparing these data with those of the American, European, and Japanese guidelines. RESULTS: Sensitivity was 100 % (95 % confidence interval [CI] 72 % to 100 %) in all models. Specificity of the artificial intelligence model and the American, European, and Japanese guidelines was 66 % (95 %CI 56 % to 76 %), 44 % (95 %CI 34 % to 55 %), 0 % (95 %CI 0 % to 3 %), and 0 % (95 %CI 0 % to 3 %), respectively; and accuracy was 69 % (95 %CI 59 % to 78 %), 49 % (95 %CI 39 % to 59 %), 9 % (95 %CI 4 % to 16 %), and 9 % (95 %CI 4 % - 16 %), respectively. The rates of unnecessary additional surgery attributable to misdiagnosing LNM-negative patients as having LNM were: 77 % (95 %CI 62 % to 89 %) for the artificial intelligence model, and 85 % (95 %CI 73 % to 93 %; P < 0.001), 91 % (95 %CI 84 % to 96 %; P < 0.001), and 91 % (95 %CI 84 % to 96 %; P < 0.001) for the American, European, and Japanese guidelines, respectively. CONCLUSIONS: Compared with current guidelines, artificial intelligence significantly reduced unnecessary additional surgery after endoscopic resection of T1 CRC without missing LNM positivity.

DOI： 10.1055/s-0043-122385

Web of Science

PubMed

CiNii Research

Correction: Artificial intelligence may help in predicting the need for additional surgery after endoscopic resection of T1 colorectal cancer Reviewed

Katsuro Ichimasa, Shin-ei Kudo, Yuichi Mori, Masashi Misawa, Shingo Matsudaira, Yuta Kouyama, Toshiyuki Baba, Eiji Hidaka, Kunihiko Wakamura, Takemasa Hayashi, Toyoki Kudo, Tomoyuki Ishigaki, Yusuke Yagawa, Hiroki Nakamura, Kenichi Takeda, Amyn Haji, Shigeharu Hamatani, Kensaku Mori, Fumio Ishida, Hideyuki Miyach

Endoscopy Vol. 50 ( 3 ) page： C2 2018.3

Correction: Artificial intelligence may help in predicting the need for additional surgery after endoscopic resection of T1 colorectal cancer Reviewed

Endoscopy Vol. 50 ( 3 ) page： C2 2018.3

Correction: Artificial intelligence may help in predicting the need for additional surgery after endoscopic resection of T1 colorectal cancer Reviewed

Endoscopy Vol. 50 ( 3 ) page： C2 2018.3

3D U-Netと測地距離カーネルを取り入れた全連結条件付き確率場に基づく医用画像からの多臓器自動抽出

楊瀛, Roth Holger, 小田昌宏, 北坂孝幸, 三澤一成, 森健策

電子情報通信学会技術研究報告(MI) Vol. 117 ( 518 ) page： 75-80 2018.3

Machine learning-based colon deformation estimation method for colonscope tracking

Masahiro Oda, Takayuki Kitasaka, Kazuhiro Furukawa, Ryoji Miyahara, Yoshiki Hirooka, Hidemi Goto, Nassir Navab, Kensaku Mori

Proc. SPIE 10576, Medical Imaging 2018 page： 1057619-1-1057619-6 2018.2

Dense volumetric detection and segmentation of mediastinal lymph nodes in chest CT images

Hirohisa Oda, Holger Roth, Kanwal K. Bhatia, Masahiro Oda, Takayuki Kitasaka, Shingo Iwano, Hirotoshi Homma, Hirotsugu Takabatake, Masaki Mori, Hiroshi Natori, Julia A. Schnabel, Kensaku Mori,

Proc. SPIE 10575, Medical Imaging 2018 page： 1057502-1-1057502-6 2018.2

Unsupervised pathology image segmantaion using representation learning with spherical k-means

Takayasu Moriya, Holger R. Roth, Shota Nakamura, Hirohisa Oda, Kai Nagara, Masahiro Oda, Kensaku Mori,

Proc. SPIE 10581, Medical Imaging 2018 page： 1058111-1-1058111-7 2018.2

Unsupervised segmentation of 3D medical images based on clustering and deep representation learning

Takayasu Moriya, Holger R. Roth, Shota Nakamura, Hirohisa Oda, Kai Nagara, Masahiro Oda, Kensaku Mori

Proc. SPIE 10578, Medical Imaging 2018 page： 105780-1-105780-7 2018.2

Towards dense volumetric pancreas segmentation in CT using 3D fully convolutional network

Holger Roth, Masahiro Oda, Natsuki Shimizu, Hirohisa Oda, Yuichiro Hayashi, Takayuki Kitasaka, Michitaka Fujiwara, Kazunari Misawa, Kensaku Mori

Proc. SPIE 10574, Medical Imaging 2018 page： 105740B-1-105740B-6 2018.2

Fine segmentation of tiny blood vessel based on fully-connected conditional random field

Chenglong Wang, Masahiro Oda, Yasushi Yoshino, Tokunori Yamamoto, Kensaku Mori

Proc. SPIE 10574, Medical Imaging 2018 page： 10740K-1-10740K-7 2018.2

Develop and Validate a Finite Element Method Model for Deformation Matching of Laparoscopic Gastrectomy Navigation

Tao Chena, Guodong Wei, Weili Shic, Yuichiro Hayashi, Masahiro Oda, Zhengang Jiang, Guoxin Li, Kensaku Mori

Proc. SPIE 10576, Medical Imaging 2018 page： 105761Y-1-10576Y-6 2018.2

医用工学と放射線技術科学との融合：期待される新技術

戸田尚宏, 小林哲生, 山谷泰賀, 有村秀孝, 内山良一, 森健策, 藤田広志, 原武史

日本放射線技術学会雑誌, 第73回総会学術大会シンポジウム2 Vol. 74 ( 2 ) page： 175-190 2018.2

医用工学と放射線技術科学との融合：期待される新技術

戸田尚宏, 小林哲生, 山谷泰賀, 有村秀孝, 内山良一, 森健策, 藤田広志, 原武史

日本放射線技術学会雑誌, 第73回総会学術大会シンポジウム2 Vol. 74 ( 2 ) page： 175-190 2018.2

術前術中後診断治療支援

森　健策

月刊「細胞] Vol. 50 ( 1 ) page： 14-18 2018.1

3Dプリンタの最新動向

森健策

インナービジョン 2018年2月号 Vol. 33 ( 2 ) page： 35-36 2018.1

3Dプリンタの最新動向

森健策

インナービジョン 2018年2月号 Vol. 33 ( 2 ) page： 35-36 2018.1

Advanced Endoscopic Navigation: Surgical Big Data, Methodology, and Applications Reviewed

Xiongbiao Luo, Kensaku Mori, Terry M. Peters

Annual Review of Biomedical Engineering Vol. 20 ( 1 ) page： 221 - 251 2018

Language：English Publishing type：Research paper (scientific journal) Publisher：Annual Reviews Inc.

Interventional endoscopy (e.g., bronchoscopy, colonoscopy, laparoscopy, cystoscopy) is a widely performed procedure that involves either diagnosis of suspicious lesions or guidance for minimally invasive surgery in a variety of organs within the body cavity. Endoscopy may also be used to guide the introduction of certain items (e.g., stents) into the body. Endoscopic navigation systems seek to integrate big data with multimodal information (e.g., computed tomography, magnetic resonance images, endoscopic video sequences, ultrasound images, external trackers) relative to the patient's anatomy, control the movement of medical endoscopes and surgical tools, and guide the surgeon's actions during endoscopic interventions. Nevertheless, it remains challenging to realize the next generation of context-aware navigated endoscopy. This review presents a broad survey of various aspects of endoscopic navigation, particularly with respect to the development of endoscopic navigation techniques. First, we investigate big data with multimodal information involved in endoscopic navigation. Next, we focus on numerous methodologies used for endoscopic navigation. We then review different endoscopic procedures in clinical applications. Finally, we discuss novel techniques and promising directions for the development of endoscopic navigation.

DOI： 10.1146/annurev-bioeng-062117-120917

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