Faculty Profiles - MORI, Kensaku

Papers - MORI, Kensaku

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内視鏡検査手術における超音波画像の利用-マルチモダリティ画像統合-

森健策

計測と制御 Vol. 58 ( 7 ) page： 541-544 2019.7

μ CT を用いた改良版 Cycle-GAN による臨床用 CT 像の超解像処理

鄭通, 小田紘久, 守谷享泰, 杉野貴明, 中村彰太, 小田昌弘, 森雅樹, 高畠博嗣, 名取博, 森健策

第38回日本医用画像工学会大会予稿集 page： OP4-02 2019.7

多元計算解剖学のその先にあるもの

森　健策

第38回日本医用画像工学会大会予稿集 page： SY2-5 2019.7

3D fully convolutional network を用いた腎腫瘍の定量評価における初期検討

王成龍, 小田昌宏, 林雄一郎, 佐々直人, 山本徳則, 森健策

第38回日本医用画像工学会大会予稿集 page： OP5-23 2019.7

深層学習を用いた非造影 CT 画像からの複数臓器領域の抽出に関する検討

林雄一郎, 申忱, Roth Holger, 小田昌宏, 三澤一成, 森健策

第38回日本医用画像工学会大会予稿集 page： OP5-14 2019.7

グラフ畳み込みニューラルネットワークを用いた腹部動脈血管名自動命名の初期検討

日比裕太, 林雄一郎, 北坂孝幸, 伊東隼人, 小田昌宏, 三澤一成, 森健策

第38回日本医用画像工学会大会予稿集 page： OP5-11 2019.7

移学習を用いた腹部 thick-slice CT 像における多臓器領域の自動抽出の初期検討

申忱, ロスホルガー, 林　雄一郎, 小田紘久, 小田昌宏, 三澤一成, 森健策

第38回日本医用画像工学会大会予稿集 page： OP4-15 2019.7

深層学習を用いた腹腔鏡手術動画像の出血領域自動セグメンテーション

山本翔太, 小田紘久, 林雄一郎, 北坂孝幸, 小田昌宏, 伊藤雅昭, 竹下修由, 森健策

第38回日本医用画像工学会大会予稿 page： OP4-13 2019.7

開腹手術映像における遮蔽物除去システムの VR 化

北坂孝幸, 伊藤幹也, 駒形和哉, 三澤一成, 森健策

第38回日本医用画像工学会大会予稿集 page： OP4-10 2019.7

3D fully convolutional network を用いた腎腫瘍の定量評価における初期検討

王成龍, 小田昌宏, 林雄一郎, 佐々直人, 山本徳則, 森健策

第38回日本医用画像工学会大会予稿集 page： OP5-23 2019.7

Artificial intelligence and upper gastrointestinal endoscopy: current status and future perspective Reviewed

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

AMED 大規模データベースを用いた CT 画像解析と病変検出への応用

森健策, 小田昌宏

第38回日本医用画像工学会大会予稿集 page： SY1-5 2019.7

Artificial intelligence and colonoscopy: Current status and future perspectives. Reviewed International journal

Kudo SE, Mori Y, Misawa M, Takeda K, Kudo T, Itoh H, Oda M, Mori K

Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society Vol. 31 ( 4 ) page： 363 - 371 2019.7

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

BACKGROUND AND AIM: Application of artificial intelligence in medicine is now attracting substantial attention. In the field of gastrointestinal endoscopy, computer-aided diagnosis (CAD) for colonoscopy is the most investigated area, although it is still in the preclinical phase. Because colonoscopy is carried out by humans, it is inherently an imperfect procedure. CAD assistance is expected to improve its quality regarding automated polyp detection and characterization (i.e. predicting the polyp's pathology). It could help prevent endoscopists from missing polyps as well as provide a precise optical diagnosis for those detected. Ultimately, these functions that CAD provides could produce a higher adenoma detection rate and reduce the cost of polypectomy for hyperplastic polyps. METHODS AND RESULTS: Currently, research on automated polyp detection has been limited to experimental assessments using an algorithm based on ex vivo videos or static images. Performance for clinical use was reported to have >90% sensitivity with acceptable specificity. In contrast, research on automated polyp characterization seems to surpass that for polyp detection. Prospective studies of in vivo use of artificial intelligence technologies have been reported by several groups, some of which showed a >90% negative predictive value for differentiating diminutive (≤5 mm) rectosigmoid adenomas, which exceeded the threshold for optical biopsy. CONCLUSION: We introduce the potential of using CAD for colonoscopy and describe the most recent conditions for regulatory approval for artificial intelligence-assisted medical devices.

DOI： 10.1111/den.13340

医用画像AI

森健策

医療機器学　第94回日本医療機器学会大会・学術集会 Vol. 89 ( 2 ) page： 107-108 2019.6

Automatic egistration of unordered point clouds for the study of abdominal organs and lymph node eformations

B. Villard, K. Tachi, K. Misawa, M. Oda, K. Mori

Computer Assisted Radiology 33rd International Congress and Exhibition CARS 2019 page： 0 2019.6

Semi-automated small intestine segmentation by fully convolutional networks and Hessian analysis

K. Mori, H. Oda, T. Sugino, K. Nishio, K. Chiba, K. Oshima, T. Kitasaka, M. Oda, C. Shirota, A. Hinoki, H. Uchida

International Journal of Computer Assisted Radiology and Surgery CARS 2019 Vol. 14 ( 1 ) page： s119-120 2019.6

3D fully convolutional network-based head structure segmentation on multi-modal images from sparse annotation

K. Mori, T. Sugino, H. Roth, M. Oda, T. Kin

International Journal of Computer Assisted Radiology and Surgery CARS 2019 Vol. 14 ( 1 ) page： s120-121 2019.6

Non-contrast to contrasted abdominal CT volume regression using fully convolutional network

M. Oda, K. K. Kumamaru, S. Aoki, K. Mori

International Journal of Computer Assisted Radiology and Surgery CARS 2019 Vol. 14 ( 1 ) page： 103-104 2019.6

Computer-based virtual clinical trial for pulmonary function diagnosis with ynamic chest radiograph

R. Tanaka, E. Samei, W. P. Segars, E. Abadi, H. Roth, H. Oda, K. Mori

International Journal of Computer Assisted Radiology and Surgery CARS 2019 Vol. 14 ( 1 ) page： s20-21 2019.6

Optical coherence tomography classification of multiple retinal diseases using DenseNet

C. Wang, M. Oda, Y. Itoh, K.Mori

International Journal of Computer Assisted Radiology and Surgery CARS 2019 Vol. 14 ( 1 ) page： s69-70 2019.6

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Papers - MORI, Kensaku