Faculty Profiles - MORI, Kensaku

Papers - MORI, Kensaku

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Improvement on Robustness of ORB-SLAM Based Surgical Navigation System by Building Submap

王成 , 小田昌宏, 林雄一郎, 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： OP2-6 2017.7

ステレオ内視鏡画像からの臓器形状復元手法における複数フレームの利用に関する初期的検討

柴田睦実, 林雄一郎, 小田昌宏, 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： OP2-8 2017.7

機械学習を用いた腹部動脈血管名自動命名における肝動脈分岐情報利用方法に関する一考察

鉄村悠介, 張暁楠, Holger Roth, 林雄一郎, 小田昌宏, 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： OP6-1 2017.7

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

page： OP11-2 2017.7

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

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Papers - MORI, Kensaku