研究者詳細 - 森　健策

論文 - 森　健策

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Cascaded 3D Fully Convolutional Networks for Medical Image Segmentation

Holger Roth, Hirohisa Oda, Xiangrong Zhou, Natsuki Shimizu, Ying Yang, Chen Shen, Yuichiro Hayashi, Masahiro Oda, Michitaka Fujiwara, Kazunari Misawa, Kensaku Mori

GTC2018, 頁： S8532 2018年3月

ディープラーニングを用いた教師なし学習によるレジストレーション手法の初期的検討

長柄　快, Holger R. Roth, 中村彰太, 小田昌宏, 森　健策

電子情報通信学会技術研究報告(MI) 117 巻 ( 518 ) 頁： 7-12 2018年3月

複数のステレオ内視鏡画像からの臓器形状復元の定量評価

柴田睦実, 林　雄一郎, 小田昌宏, 三澤一成, 森　健策

電子情報通信学会技術研究報告(MI) 117 巻 ( 518 ) 頁： 117-122 2018年3月

CNNによる回帰を用いた臓器領域の位置推定手法の初期的検討

清水南月, 小田昌宏, ロスホルガー, 林　雄一郎, 三澤一成, 藤原道隆, 森　健策

電子情報通信学会技術研究報告(MI) 117 巻 ( 518 ) 頁： 81-86 2018年3月

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) 117 巻 ( 518 ) 頁： 75-80 2018年3月

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) 117 巻 ( 518 ) 頁： 51-56 2018年3月

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) 117 巻 ( 518 ) 頁： 31-32 2018年3月

MICCAI2017参加報告

大竹義人, 伊藤康一, 小田昌宏, 備瀬竜馬, 諸岡健一, 周　向栄, 斉藤　篤, 清水昭伸, 増谷佳孝, 佐藤嘉伸, 森　健策

電子情報通信学会技術研究報告(MI) 117 巻 ( 518 ) 頁： 125-131 2018年3月

Pre/intra-operative diagnosis and navigational assistance based on multidisciplinary computational anatomy

Kensaku Mori, Masahiro Oda, Holger R roth, Yoshihiko Nakamura, Yoshito Mekada, Takayuki Kitasaka, Kazunari Misawa, Michitaka Fujiwara, Kazuhiro Durukawa, Shu Ichihara

頁： 87-105 2018年3月

Artificial intelligence may help in predicting the need for additional surgery after endoscopic resection of T1 colorectal cancer 査読有り国際誌

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

ENDOSCOPY 50 巻 ( 3 ) 頁： 230 - 240 2018年3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：Georg Thieme Verlag KG

<jats:title>Abstract</jats:title><jats:p>
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.</jats:p><jats:p>
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.</jats:p><jats:p>
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.</jats:p><jats:p>
Conclusions Compared with current guidelines, artificial intelligence significantly reduced unnecessary additional surgery after endoscopic resection of T1 CRC without missing LNM positivity.</jats:p>

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 査読有り

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 50 巻 ( 3 ) 頁： C2 2018年3月

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

Endoscopy 50 巻 ( 3 ) 頁： C2 2018年3月

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

Endoscopy 50 巻 ( 3 ) 頁： C2 2018年3月

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) 117 巻 ( 518 ) 頁： 75-80 2018年3月

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 頁： 1057619-1-1057619-6 2018年2月

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 頁： 1057502-1-1057502-6 2018年2月

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 頁： 105780-1-105780-7 2018年2月

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 頁： 1058111-1-1058111-7 2018年2月

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 頁： 105740B-1-105740B-6 2018年2月

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 頁： 10740K-1-10740K-7 2018年2月

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