Faculty Profiles - MORI, Kensaku

Papers - MORI, Kensaku

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CT画像XAI技術で「新型コロナウィルス肺炎」を85%精度で識別

小田昌宏, 森健策

C-press Vol. 120 page： 5 - 6 2021.2

COVID-19診断支援AI開発における名古屋大学の取り組み

小田昌宏, 鄭通, 林雄一郎, 森健策

Vol. 39 ( 1 ) page： 13 - 19 2021.2

New method for the assessment of perineural invasion from perihilar cholangiocarcinoma Reviewed

Hiroshi Tanaka, Tsuyoshi Igami, Yoshie Shimoyama, Tomoki Ebata, Yukihiro Yokoyama, Kensaku Mori, Masato Nagino,

Surgery Today Vol. 51 ( 2 ) page： 136 - 143 2021.1

Current status and future perspective on artificial intelligence for lower endoscopy. Invited International journal

Masashi Misawa, Shin-Ei Kudo, Yuichi Mori, Yasuharu Maeda, Yushi Ogawa, Katsuro Ichimasa, Toyoki Kudo, Kunihiko Wakamura, Takemasa Hayashi, Hideyuki Miyachi, Toshiyuki Baba, Fumio Ishida, Hayato Itoh, Masahiro Oda, Kensaku Mori

Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society Vol. 33 ( 2 ) page： 273 - 284 2021.1

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

The global incidence and mortality rate of colorectal cancer remains high. Colonoscopy is regarded as the gold standard examination for detecting and eradicating neoplastic lesions. However, there are some uncertainties in colonoscopy practice that are related to limitations in human performance. First, approximately one-fourth of colorectal neoplasms are missed on a single colonoscopy. Second, it is still difficult for non-experts to perform adequately regarding optical biopsy. Third, recording of some quality indicators (e.g. cecal intubation, bowel preparation, and withdrawal speed) which are related to adenoma detection rate, is sometimes incomplete. With recent improvements in machine learning techniques and advances in computer performance, artificial intelligence-assisted computer-aided diagnosis is being increasingly utilized by endoscopists. In particular, the emergence of deep-learning, data-driven machine learning techniques have made the development of computer-aided systems easier than that of conventional machine learning techniques, the former currently being considered the standard artificial intelligence engine of computer-aided diagnosis by colonoscopy. To date, computer-aided detection systems seem to have improved the rate of detection of neoplasms. Additionally, computer-aided characterization systems may have the potential to improve diagnostic accuracy in real-time clinical practice. Furthermore, some artificial intelligence-assisted systems that aim to improve the quality of colonoscopy have been reported. The implementation of computer-aided system clinical practice may provide additional benefits such as helping in educational poorly performing endoscopists and supporting real-time clinical decision-making. In this review, we have focused on computer-aided diagnosis during colonoscopy reported by gastroenterologists and discussed its status, limitations, and future prospects.

DOI： 10.1111/den.13847

Web of Science

Scopus

PubMed

Dense-layer-based YOLO-v3 for detection and localization of colon perforations Invited Reviewed

Kai Jiang, Hayato Itoh, Masahiro Oda, Taishi Okumura, Yuichi Mori, Masashi Misawa, Takemasa Hayashi, Shin-Ei Kudo, Kensaku Mori

Medical Imaging 2021: Computer-Aided Diagnosis Vol. 11597 page： 115971A-1 - 6 2021

Robust endocytoscopic image classification based on higher-order symmetric tensor analysis and multi-scale topological statistics. Invited Reviewed International journal

Hayato Itoh, Yukitaka Nimura, Yuichi Mori, Masashi Misawa, Shin-Ei Kudo, Kinichi Hotta, Kazuo Ohtsuka, Shoichi Saito, Yutaka Saito, Hiroaki Ikematsu, Yuichiro Hayashi, Masahiro Oda, Kensaku Mori

International journal of computer assisted radiology and surgery Vol. 15 ( 12 ) page： 2049 - 2059 2020.12

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

PURPOSE: An endocytoscope is a new type of endoscope that enables users to perform conventional endoscopic observation and ultramagnified observation at the cell level. Although endocytoscopy is expected to improve the cost-effectiveness of colonoscopy, endocytoscopic image diagnosis requires much knowledge and high-level experience for physicians. To circumvent this difficulty, we developed a robust endocytoscopic (EC) image classification method for the construction of a computer-aided diagnosis (CAD) system, since real-time CAD can resolve accuracy issues and reduce interobserver variability. METHOD: We propose a novel feature extraction method by introducing higher-order symmetric tensor analysis to the computation of multi-scale topological statistics on an image, and we integrate this feature extraction with EC image classification. We experimentally evaluate the classification accuracy of our proposed method by comparing it with three deep learning methods. We conducted this comparison by using our large-scale multi-hospital dataset of about 55,000 images of over 3800 patients. RESULTS: Our proposed method achieved an average 90% classification accuracy for all the images in four hospitals even though the best deep learning method achieved 95% classification accuracy for images in only one hospital. In the case with a rejection option, the proposed method achieved expert-level accurate classification. These results demonstrate the robustness of our proposed method against pit pattern variations, including differences of colours, contrasts, shapes, and hospitals. CONCLUSIONS: We developed a robust EC image classification method with novel feature extraction. This method is useful for the construction of a practical CAD system, since it has sufficient generalisation ability.

DOI： 10.1007/s11548-020-02255-3

Web of Science

Scopus

PubMed

[総論]AI時代を見据えた消化器外科手術 AIによる大腸T2癌リンパ節転移予測 Invited Reviewed

中原健太, 石田文生, 一政克朗, 森悠一, 三澤将史, 澤田成彦, 工藤進英, Villard Ben, 伊東隼人, 森健策

日本消化器外科学会総会 Vol. 75回 page： WS15 - 6 2020.12

Improving contrast and spatial resolution in crystal analyzer based x ray dark field imaging Reviewed

Masami Ando Yuki Nakao Ge Jin Hiroshi Sugiyama Naoki Sunaguchi Yongjin Sung Yoshifumi Suzuki Yong Sun Michio Tanimoto Katsuhiro Kawashima Tetsuya Yuasa Kensaku Mori Shu Ichihara Rajiv Gupta,

Medical Physics Vol. 47 ( 11 ) page： 5505 - 5513 2020.11

多元計算解剖モデルと人工知能に基づく診断治療支援 Invited Reviewed

森　健策

映像情報メディア学会誌 Vol. 74 ( 6 ) page： 909-915 2020.11

Preliminary study of Loss-Function Design for Detection and Localization of Perforations with YOLO-v3 in Colonoscopic Images

Vol. 22 ( 4 ) page： 348 - 349 2020.11

腸閉塞およびイレウスの診断支援システムにおける距離マップの導入

小田紘久, 林雄一郎, 北坂孝幸，玉田雄大,滝本愛太朗，檜顕成, 内田広夫，鈴木耕次郎, 伊東隼人，小田昌宏，森　健策

日本コンピュータ外科学会誌第29回日本コンピュータ外科学会大会特集号 Vol. 22 ( 4 ) page： 282 - 284 2020.11

局所情報に注目した腹腔鏡動画像からの出血領域抽出

山本翔太, 盛満慎太郎，林　雄一郎，北坂　孝幸，小田　昌宏，伊藤　雅昭，竹下　修由,　森　健策

日本コンピュータ外科学会誌第29回日本コンピュータ外科学会大会特集号 Vol. 22 ( 4 ) page： 285 - 286 2020.11

Dilated convolution を用いた腹腔鏡動画像からの血管領域抽出における空間情報利用に関する検討

盛満慎太郎, 山本翔太, 小澤卓也, 北坂孝幸, 林　雄一郎, 小田昌宏, 伊藤雅昭, 竹下修由，三澤一成, 森　健策

日本コンピュータ外科学会誌第29回日本コンピュータ外科学会大会特集号 Vol. 22 ( 4 ) page： 287 - 288 2020.11

表現学習に基づくクラスタリングによるCOVID-19 肺CT像からの病変部抽出手法

鄭通, 小田昌宏, 王成龍，林　雄一郎，橋本正弘, 大竹義人，明石敏昭, 森　健策

日本コンピュータ外科学会誌第29回日本コンピュータ外科学会大会特集号 Vol. 22 ( 4 ) page： 294 - 295 2020.11

深層学習によるMRI画像からの神経鞘腫の自動位置検出

小田昌宏, 伊藤定之, 今釜史郎, 森健策

日本コンピュータ外科学会誌第29回日本コンピュータ外科学会大会特集号 Vol. 22 ( 4 ) page： 296 - 297 2020.11

腹腔鏡動画像用オンラインアノテーションツールの開発

屠芸豪, 伊東隼人，小澤卓也，小田昌宏, 竹下修由，伊藤雅昭，森健策

日本コンピュータ外科学会誌第29回日本コンピュータ外科学会大会特集号 Vol. 22 ( 4 ) page： 306 - 307 2020.11

呼吸器外科における仮想胸腔鏡像による手術ナビゲーションシステムを用いた手術支援の検討

林雄一郎, 中村彰太，森健策

日本コンピュータ外科学会誌第29回日本コンピュータ外科学会大会特集号 Vol. 22 ( 4 ) page： 337 2020.11

気管支鏡ナビゲーションのための敵対的生成による内視鏡画像深度推定の評価

王成, 小田昌宏，林雄一郎，北坂孝幸，本間裕敏，高畠博嗣，森雅樹，名取博, 森　健策

日本コンピュータ外科学会誌第29回日本コンピュータ外科学会大会特集号 Vol. 22 ( 4 ) page： 338 - 339 2020.11

SUN database : 大腸ポリープ自動検出器の精度評価に向けた試験用画像

伊東隼人, 三澤将史，森悠一，小田　昌宏，工藤　進英, 森健策

日本コンピュータ外科学会誌第29回日本コンピュータ外科学会大会特集号 Vol. 22 ( 4 ) page： 346 - 347 2020.11

Cost savings in colonoscopy with artificial intelligence-aided polyp diagnosis: an add-on analysis of a clinical trial (with video). Invited Reviewed International journal

Yuichi Mori, Shin-Ei Kudo, James E East, Amit Rastogi, Michael Bretthauer, Masashi Misawa, Masau Sekiguchi, Takahisa Matsuda, Yutaka Saito, Hiroaki Ikematsu, Kinichi Hotta, Kazuo Ohtsuka, Toyoki Kudo, Kensaku Mori

Gastrointestinal endoscopy Vol. 92 ( 4 ) page： 905 - 911 2020.10

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

BACKGROUND AND AIMS: Artificial intelligence (AI) is being implemented in colonoscopy practice, but no study has investigated whether AI is cost saving. We aimed to quantify the cost reduction using AI as an aid in the optical diagnosis of colorectal polyps. METHODS: This study is an add-on analysis of a clinical trial that investigated the performance of AI for differentiating colorectal polyps (ie, neoplastic versus non-neoplastic). We included all patients with diminutive (≤5 mm) rectosigmoid polyps in the analyses. The average colonoscopy cost was compared for 2 scenarios: (1) a diagnose-and-leave strategy supported by the AI prediction (ie, diminutive rectosigmoid polyps were not removed when predicted as non-neoplastic), and (2) a resect-all-polyps strategy. Gross annual costs for colonoscopies were also calculated based on the number and reimbursement of colonoscopies conducted under public health insurances in 4 countries. RESULTS: Overall, 207 patients with 250 diminutive rectosigmoid polyps (104 neoplastic, 144 non-neoplastic, and 2 indeterminate) were included. AI correctly differentiated neoplastic polyps with 93.3% sensitivity, 95.2% specificity, and 95.2% negative predictive value. Thus, 105 polyps were removed and 145 were left under the diagnose-and-leave strategy, which was estimated to reduce the average colonoscopy cost and the gross annual reimbursement for colonoscopies by 18.9% and US$149.2 million in Japan, 6.9% and US$12.3 million in England, 7.6% and US$1.1 million in Norway, and 10.9% and US$85.2 million in the United States, respectively, compared with the resect-all-polyps strategy. CONCLUSIONS: The use of AI to enable the diagnose-and-leave strategy results in substantial cost reductions for colonoscopy.

DOI： 10.1016/j.gie.2020.03.3759

Scopus

PubMed

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Papers - MORI, Kensaku