研究者詳細 - 森　健策

論文 - 森　健策

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Intestinal region reconstruction of ileus cases from 3D CT images based on graphical representation and its visualization 査読有り

Hirohisa Oda, Yuichiro Hayashi, Takayuki Kitasaka, Yudai Tamada, Aitaro Takimoto, Akinari Hinoki, Hiroo Uchida, Kojiro Suzuki, Hayato Itoh, Masahiro Oda, Kensaku Mori

Proc.SPIE 11597, Medical Imaging 2021: Computer-Aided Diagnosis 2021年2月

Lung infection and normal region segmentation from CT volumes of COVID-19 cases 査読有り Open Access

Masahiro Oda, Yuichiro Hayashi, Yoshito Otake, Masahiro Hashimoto, Toshiaki Akashi, Kensaku Mori

Proc. SPIE 11597, Medical Imaging 2021: Computer-Aided Diagnosis 頁： 115972X-1 - 6 2021年2月

Extraction of lung and lesion regions from COVID-19 CT volumes using 3D fully convolutional networks 査読有り

Yuichiro Hayashi, Masahiro Oda, Chen Shen, Masahiro Hashimoto, Yoshito Otake, Toshiaki Akashi, Kensaku Mori

Proc.SPIE 11597, Medical Imaging 2021: Computer-Aided Diagnosis 頁： 115972A-1 - 6 2021年2月

人工知能（AI）最新動向　AI研究から見たRSNA-AIの広がりを感じる大会招待有り

森健策

インナービジョン 36 巻 ( 2 ) 頁： 25 - 26 2021年2月

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 120 巻頁： 5 - 6 2021年2月

COVID-19診断支援AI開発における名古屋大学の取り組み Open Access

小田昌宏, 鄭通, 林雄一郎, 森健策

39 巻 ( 1 ) 頁： 13 - 19 2021年2月

New method for the assessment of perineural invasion from perihilar cholangiocarcinoma 査読有り

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

Surgery Today 51 巻 ( 2 ) 頁： 136 - 143 2021年1月

Current status and future perspective on artificial intelligence for lower endoscopy 招待有り国際誌 Open Access

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

DIGESTIVE ENDOSCOPY 33 巻 ( 2 ) 頁： 273 - 284 2021年1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

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 招待有り査読有り

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

MEDICAL IMAGING 2021: COMPUTER-AIDED DIAGNOSIS 11597 巻頁： 115971A-1 - 6 2021年

記述言語：日本語掲載種別：研究論文（国際会議プロシーディングス）出版者・発行元：Progress in Biomedical Optics and Imaging - Proceedings of SPIE

Endoscopic submucosal dissection is a minimally invasive treatment for early gastric cancer. In endoscopic submucosal dissection, a physician directly removes the mucosa around the lesion under internal endoscopy by using the flush knife. However, the flush knife may accidentally pierce the colonic wall and generate a perforation on it. If physicians overlooking a small perforation, a patient may need emergency open surgery, since a perforation can easily cause peritonitis. For the prevention of overlooking of perforations, a computer-aided diagnosis system has a potential demand. We believe automatic perforation detection and localization function is very useful for the analysis of endoscopic submucosal dissection videos for the development of a computeraided diagnosis system. At current stage, the research of perforation detection and localization progress slowly, automatic image-based perforation detection is very challenge. Thus, we devote to the development of detection and localization of perforations in colonoscopic videos. In this paper, we proposed a supervised-learning method for perforations detection and localization in colonoscopic videos. This method uses dense layers in YOLO-v3 instead of residual units, and a combination of binary cross entropy and generalized intersection over union loss as the loss function in the training process. This method achieved 0.854 accuracy, 0.850 AUC score and 0.884 mean average precision for perforation detection and localization, respectively, as an initial study

DOI： 10.1117/12.2582300

Web of Science

Scopus

Attention-Guided Pancreatic Duct Segmentation from Abdominal CT Volumes 招待有り査読有り

Shen, C; Roth, HR; Yuichiro, H; Oda, M; Miyamoto, T; Sato, G; Mori, K

CLINICAL IMAGE-BASED PROCEDURES, DISTRIBUTED AND COLLABORATIVE LEARNING, ARTIFICIAL INTELLIGENCE FOR COMBATING COVID-19 AND SECURE AND PRIVACY-PRESERVING MACHINE LEARNING, CLIP 2021, DCL 2021, LL-COVID19 2021, PPML 2021 12969 巻頁： 46 - 55 2021年

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）出版者・発行元：Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics

Pancreatic duct dilation indicates a high risk of pancreatic ductal adenocarcinoma (PDAC), the deadliest cancer with a poor prognosis. Segmentation of dilated pancreatic duct from CT taken from patients without PDAC shows the potential to assist the early detection of PDAC. Most current researches include pancreatic duct segmentation as one additional class for patients who have already detected PDAC. However, the dilated pancreatic duct for people who have not yet developed PDAC is typically much smaller, making the segmentation difficult. Deep learning-based segmentation on tiny components is challenging because of the large imbalance between the target object and irrelevant regions. In this work, we explore an attention-guided approach for dilated pancreatic duct segmentation as a screening tool for pre-PDAC patients, enhancing the pancreas regions’ concentration and ignoring the unnecessary features. We employ a multi-scale aggregation to combine the information at different scales to improve the segmentation performance further. Our proposed multi-scale pancreatic attention-guided approach achieved a Dice score of 54.16% on dilated pancreatic duct dataset, which shows a significant improvement over prior techniques.

DOI： 10.1007/978-3-030-90874-4_5

Web of Science

Scopus

その他リンク： https://dblp.uni-trier.de/db/conf/clip-ws/clip2021.html#ShenRHOMSM21

Robust endocytoscopic image classification based on higher-order symmetric tensor analysis and multi-scale topological statistics 招待有り査読有り国際誌 Open Access

Itoh, H; Nimura, Y; Mori, Y; Misawa, M; Kudo, SE; Hotta, K; Ohtsuka, K; Saito, S; Saito, Y; Ikematsu, H; Hayashi, Y; Oda, M; Mori, K

INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY 15 巻 ( 12 ) 頁： 2049 - 2059 2020年12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：International Journal of Computer Assisted Radiology and Surgery

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

[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招待有り査読有り

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 75回巻頁： WS15 - 6 2020年12月

Improving contrast and spatial resolution in crystal analyzer based x ray dark field imaging 査読有り Open Access

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 47 巻 ( 11 ) 頁： 5505 - 5513 2020年11月

多元計算解剖モデルと人工知能に基づく診断治療支援招待有り査読有り Open Access

森　健策

映像情報メディア学会誌 74 巻 ( 6 ) 頁： 909-915 2020年11月

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

22 巻 ( 4 ) 頁： 348 - 349 2020年11月

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 22 巻 ( 4 ) 頁： 294 - 295 2020年11月

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 22 巻 ( 4 ) 頁： 296 - 297 2020年11月

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 22 巻 ( 4 ) 頁： 306 - 307 2020年11月

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 22 巻 ( 4 ) 頁： 337 2020年11月

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 22 巻 ( 4 ) 頁： 338 - 339 2020年11月

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