Faculty Profiles - MORI, Kensaku

Papers - MORI, Kensaku

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CT 像から抽出した腹部動脈領域におけるCNN を用いた過検出削減でのパッチ画像生成手法の検討

小田昌宏, 山本徳則, 吉野能, 森健策

第36回日本医用画像工学会大会予稿集 page： OP11-7 2017.7

マイクロ CT 画像情報を利用した特徴点対応付けに基づく顕微鏡画像の 3 次元再構築

長柄快, Holger R. ROTH, 中村彰太, 小田紘久, 守谷享泰, 小田昌宏, 森健策

第36回日本医用画像工学会大会予稿集 page： OP14-1 2017.7

血管情報を用いた経時リンパ節の自動対応付け手法に関する研究

舘高基, 小田昌宏, 中村嘉彦, 寶珠山裕, 三澤一成, 森健策

第36回日本医用画像工学会大会予稿集 page： OP15-4 2017.7

A Study on Fine Blood Vessel Segmentation Using Fully-connected Conditional Random Field

page： OP11-2 2017.7

3DU-Netによる3次元胸部CT像からのリンパ節検出

小田紘久, KanwalK.Bhatia, HolgerR.Roth, 小田昌宏, 北坂孝幸, 岩野信吾, 本間裕敏, 高畠博嗣, 森雅樹, 名取博, JuliaA.Schnabel, 森健策

第36回日本医用画像工学会大会予稿集 page： OP1-6 2017.7

Feature-based registration of micro CT volumes

Kai Nagara, Shota Nakamura, Hoiger R. Roth, Masahiro Oda, Hirotoshi Homma, Hirotsugu Takabatake, Masaki Mori, Hiroshi Natori, Kesaku Mori

International Journal of Computer Assisted Radiology and Surgery Vol. 12 page： S201-S203 2017.6

Multi-organ segmentation in abdominal CT using 3D fully convolutional networks

Holger R. Roth, Masahiro Oda, Yuichiro Hayashi, Hirohisa Oda, Kensaku Mori

International Journal of Computer Assisted Radiology and Surgery Vol. 12 page： S55-S57 2017.6

Feature-based registration of micro CT volumes

Kai Nagara, Shota Nakamura, Hoiger R. Roth, Masahiro Oda, Hirotoshi Homma, Hirotsugu Takabatake, Masaki Mori, Hiroshi Natori, Kesaku Mori

International Journal of Computer Assisted Radiology and Surgery Vol. 12 page： S201-S203 2017.6

Multi-organ segmentation in abdominal CT using 3D fully convolutional networks

Holger R. Roth, Masahiro Oda, Yuichiro Hayashi, Hirohisa Oda, Kensaku Mori

International Journal of Computer Assisted Radiology and Surgery Vol. 12 page： S55-S57 2017.6

Endocytoscope image classification using deep convolutional neural networks

Masahiro Oda, Yutaka Hoshiyama, Masashi Misawa, Yuichi Mori, Kenichi Takeda, Sin-ei Kudo, Kensaku Mori

International Journal of Computer Assisted Radiology and Surgery Vol. 12 page： S147-S148 2017.6

False positive reduction of abdominal artery segmentation from CT volumes based on deep convolutional neural networks

Masahiro Oda, Tokunori Yamamoto, Yasushi Yoshino, Kensaku Mori

International Journal of Computer Assisted Radiology and Surgery Vol. 12 page： S27-S29 2017.6

Endocytoscope image classification using deep convolutional neural networks

Masahiro Oda, Yutaka Hoshiyama, Masashi Misawa, Yuichi Mori, Kenichi Takeda, Sin-ei Kudo, Kensaku Mori

International Journal of Computer Assisted Radiology and Surgery Vol. 12 page： S147-S148 2017.6

False positive reduction of abdominal artery segmentation from CT volumes based on deep convolutional neural networks

Masahiro Oda, Tokunori Yamamoto, Yasushi Yoshino, Kensaku Mori

International Journal of Computer Assisted Radiology and Surgery Vol. 12 page： S27-S29 2017.6

Automatic Anatomical Labeling of Arteries and Veins Using Conditional Random Fields

Takayuki Kitasaka, Mitsuru Kagajo, Yukitaka Nimura, Yuichiro Hayashi, Masahiro Oda, Kazunari Misawa, Kensaku Mori

8th International Conference on Information Processing in Computer-Assisted Interventions (IPCAI 2017) page： - 2017.6

Automatic Anatomical Labeling of Arteries and Veins Using Conditional Random Fields

Takayuki Kitasaka, Mitsuru Kagajo, Yukitaka Nimura, Yuichiro Hayashi, Masahiro Oda, Kazunari Misawa, Kensaku Mori

8th International Conference on Information Processing in Computer-Assisted Interventions (IPCAI 2017) page： - 2017.6

Automatic anatomical labeling of arteries and veins using conditional random fields Reviewed

Takayuki Kitasaka, Mitsuru Kagajo, Yukitaka Nimura, Yuichiro Hayashi, Masahiro Oda, Kazunari Misawa, Kensaku Mori

INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY Vol. 12 ( 6 ) page： 1041 - 1048 2017.6

Language：English Publishing type：Research paper (scientific journal) Publisher：SPRINGER HEIDELBERG

For safe and reliable laparoscopic surgery, it is important to determine individual differences of blood vessels such as the position, shape, and branching structures. Consequently, a computer-assisted laparoscopy that displays blood vessel structures with anatomical labels would be extremely beneficial. This paper details an automated anatomical labeling method for abdominal arteries and veins extracted from 3D CT volumes.
The proposed method represents a blood vessel tree as a probabilistic graphical model by conditional random fields (CRFs). An adaptive gradient algorithm is adopted for structure learning. The anatomical labeling of blood vessel branches is performed by maximum a posteriori estimation.
We applied the proposed method to 50 cases of arterial and portal phase abdominal X-ray CT volumes. The experimental results showed that the F-measure of the proposed method for abdominal arteries and veins was 94.4 and 86.9%, respectively.
We developed an automated anatomical labeling method to annotate each blood vessel branches of abdominal arteries and veins using CRF. The proposed method outperformed a state-of-the-art method.

DOI： 10.1007/s11548-017-1549-x

Web of Science

Scopus

PubMed

Accuracy of computer-aided diagnosis based on narrow-band imaging endocytoscopy for diagnosing colorectal lesions: comparison with experts Reviewed

Masashi Misawa, Shin-ei Kudo, Yuichi Mori, Kenichi Takeda, Yasuharu Maeda, Shinichi Kataoka, Hiroki Nakamura, Toyoki Kudo, Kunihiko Wakamura, Takemasa Hayashi, Atsushi Katagiri, Toshiyuki Baba, Fumio Ishida, Haruhiro Inoue, Yukitaka Nimura, Msahiro Oda, Kensaku Mori

INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY Vol. 12 ( 5 ) page： 757 - 766 2017.6

Language：English Publishing type：Research paper (scientific journal) Publisher：SPRINGER HEIDELBERG

Real-time characterization of colorectal lesions during colonoscopy is important for reducing medical costs, given that the need for a pathological diagnosis can be omitted if the accuracy of the diagnostic modality is sufficiently high. However, it is sometimes difficult for community-based gastroenterologists to achieve the required level of diagnostic accuracy. In this regard, we developed a computer-aided diagnosis (CAD) system based on endocytoscopy (EC) to evaluate cellular, glandular, and vessel structure atypia in vivo. The purpose of this study was to compare the diagnostic ability and efficacy of this CAD system with the performances of human expert and trainee endoscopists.
We developed a CAD system based on EC with narrow-band imaging that allowed microvascular evaluation without dye (ECV-CAD). The CAD algorithm was programmed based on texture analysis and provided a two-class diagnosis of neoplastic or non-neoplastic, with probabilities. We validated the diagnostic ability of the ECV-CAD system using 173 randomly selected EC images (49 non-neoplasms, 124 neoplasms). The images were evaluated by the CAD and by four expert endoscopists and three trainees. The diagnostic accuracies for distinguishing between neoplasms and non-neoplasms were calculated.
ECV-CAD had higher overall diagnostic accuracy than trainees (87.8 vs 63.4%; ), but similar to experts (87.8 vs 84.2%; ). With regard to high-confidence cases, the overall accuracy of ECV-CAD was also higher than trainees (93.5 vs 71.7%; ) and comparable to experts (93.5 vs 90.8%; ).
ECV-CAD showed better diagnostic accuracy than trainee endoscopists and was comparable to that of experts. ECV-CAD could thus be a powerful decision-making tool for less-experienced endoscopists.

DOI： 10.1007/s11548-017-1542-4

Web of Science

Scopus

PubMed

機械学習を用いた内視鏡画像自動診断

第56回日本生体医工学会大会プログラム・抄録集 page： 344 2017.5

3Dプリンタの医療応用

森　健策

医用画像情報学会雑誌 Vol. 34 ( 1 ) page： 1-6 2017.4

3Dプリンタの医療応用

森健策

医用画像情報学会雑誌 Vol. 34 ( 1 ) page： 1-6 2017.4

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Papers - MORI, Kensaku