Faculty Profiles - ODA, Masahiro

Papers - ODA, Masahiro

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Improvement of robustness of SLAM-based bronchoscope tracking by posture guided feature matching

Cheng Wang, Masahiro Oda, Yuichiro Hayashi, Hirotoshi Honma, Hirotsugu Takabatake, Masaki Mori, Hiroshi Natori, Kensaku Mori

International Journal of Computer Assisted Radiology and Surgery Vol. 13 ( 1 ) page： s11-12 2018.6

Evaluation of 3D fully convolutional networks for multi-class organ segmentation in contrast-enhanced CT

Chen Shen, Holger R. Roth, Hirohisa Oda, Masahiro Oda, Yuichiro Hayashi, Kazunari Misawa, Tadaaki Miyamoto, Kensaku Mori

International Journal of Computer Assisted Radiology and Surgery Vol. 13 ( 1 ) page： s21-22 2018.6

Artificial Intelligence-Assisted Polyp Detection for Colonoscopy: Initial Experience Reviewed

Misawa Masashi, Kudo Shin-ei, Mori Yuichi, Cho Tomonari, Kataoka Shinichi, Yamauchi Akihiro, Ogawa Yushi, Maeda Yasuharu, Takeda Kenichi, Ichimasa Katsuro, Nakamura Hiroki, Yagawa Yusuke, Toyoshima Naoya, Ogata Noriyuki, Kudo Toyoki, Hisayuki Tomokazu, Hayashi Takemasa, Wakamura Kunihiko, Baba Toshiyuki, Ishida Fumio, Itoh Hayato, Roth Holger, Oda Masahiro, Mori Kensaku

GASTROENTEROLOGY Vol. 154 ( 8 ) page： 2027 - + 2018.6

DIAGNOSTIC IMAGING SYSTEM WITH VIRTUAL ENTEROSCOPY AND COMPUTER-AIDED DETECTION FOR EVALUATION OF SMALL BOWEL LESIONS OF CROHN'S DISEASE Reviewed

Furukawa Kazuhiro, Miyahara Ryoji, Funasaka Kohei, Suhara Hiroki, Matsushita Masanobu, Yamamura Takeshi, Ishikawa Takuya, Ohno Eizaburo, Nakamura Masanao, Kawashima Hiroki, Watanabe Osamu, Oda Masahiro, Mori Kensaku, Hirooka Yoshiki, Goto Hidemi

GASTROINTESTINAL ENDOSCOPY Vol. 87 ( 6 ) page： AB304-AB304 2018.6

An application of cascaded 3D fully convolutional networks for medical image segmentation Reviewed

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

COMPUTERIZED MEDICAL IMAGING AND GRAPHICS Vol. 66 page： 90 - 99 2018.6

DIAGNOSTIC IMAGING SYSTEM WITH VIRTUAL ENTEROSCOPY AND COMPUTER-AIDED DETECTION FOR EVALUATION OF SMALL BOWEL LESIONS OF CROHN'S DISEASE Reviewed

GASTROINTESTINAL ENDOSCOPY Vol. 87 ( 6 ) page： AB304-AB304 2018.6

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) Vol. 117 ( 518 ) page： 75-80 2018.3

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) Vol. 117 ( 518 ) page： 81-86 2018.3

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

page： 87-105 2018.3

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) Vol. 117 ( 518 ) page： 7-12 2018.3

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) Vol. 117 ( 518 ) page： 117-122 2018.3

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) Vol. 117 ( 518 ) page： 51-56 2018.3

Develop and Validate a Finite Element Method Model for Deformation Matching of Laparoscopic Gastrectomy Navigation Reviewed

Chen Tao, Wei Guodong, Shi Weili, Hayashi Yuichiro, Oda Masahiro, Jiang Zhengang, Li Guoxin, Mori Kensaku

MEDICAL IMAGING 2018: IMAGE-GUIDED PROCEDURES, ROBOTIC INTERVENTIONS, AND MODELING Vol. 10576 2018

Micro Anatomical Structure Imaging by Desktop Micro-focus X-ray CT Scanner Invited

MORI Kensaku, NAKAMURA Shota, AKITA Toshiaki, ODA Hirohisa, Holger ROTH R., ODA Masahiro

Medical Imaging Technology Vol. 36 ( 3 ) page： 127-132 2018

Language：Japanese Publishing type：Research paper (conference, symposium, etc.)

This paper shows examples of micro anatomical structure imaging using a desktop micro-focus X-ray CT scanner. Currently clinical X-ray CT scanners can take CT images in 0.5mm to 1mm per voxel in resolution. Such scanner can take anatomical structures close to image resolution of scanners. Micro-focus X-ray CT (micro CT) scanner can take CT images of 1μm per voxel to 50μm per voxel in resolution. Such scanner enables us to explore micro anatomical structures. This paper shows several examples of micro CT images of lung and heart specimens with discussion on future direction.

DOI： 10.11409/mit.36.127

Guidance of Deep Learning for Medical Image Processing: Introduction Invited

ODA Masahiro

Medical Imaging Technology Vol. 36 ( 2 ) page： 45 - 46 2018

Towards Automated Colonoscopy Diagnosis: Binary Polyp Size Estimation via Unsupervised Depth Learning Reviewed

Itoh Hayato, Roth Holger R., Lu Le, Oda Masahiro, Misawa Masashi, Mori Yuichi, Kudo Shin-ei, Mori Kensaku

MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2018, PT II Vol. 11071 page： 611 - 619 2018

Towards dense volumetric pancreas segmentation in CT using 3D fully convolutional networks

Roth Holger, Oda Masahiro, Shimizu Natsuki, Oda Hirohisa, Hayashi Yuichiro, Kitasaka Takayuki, Fujiwara Michitaka, Misawa Kazunari, Mori Kensaku

MEDICAL IMAGING 2018: IMAGE PROCESSING Vol. 10574 2018

Unsupervised Pathology Image Segmentation Using Representation Learning with Spherical K-means Reviewed

Moriya Takayasu, Roth Holger H., Nakamura Shota, Oda Hirohisa, Nagara Kai, Oda Masahiro, Mori Kensaku

MEDICAL IMAGING 2018: DIGITAL PATHOLOGY Vol. 10581 2018

Unsupervised Segmentation of 3D Medical Images Based on Clustering and Deep Representation Learning Reviewed

Moriya Takayasu, Roth Holger R., Nakamura Shota, Oda Hirohisa, Nagara Kai, Oda Masahiro, Mori Kensaku

MEDICAL IMAGING 2018: BIOMEDICAL APPLICATIONS IN MOLECULAR, STRUCTURAL, AND FUNCTIONAL IMAGING Vol. 10578 2018

Important Points to Take Advantage of Deep Learning Invited

ODA Masahiro

Medical Imaging Technology Vol. 36 ( 2 ) page： 72-75 2018

Authorship：Lead author Language：Japanese Publishing type：Research paper (conference, symposium, etc.)

Deep learning can be applied to many tasks in medical image processing. There are some points to be careful in research use of deep learning to take advantage of its performance. Among such points, three topics including manual specification of many parameters in deep learning, pitfalls of data augmentation, and how randomly selected parameters in deep learning affect its performance, are discussed here.

DOI： 10.11409/mit.36.72

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Papers - ODA, Masahiro