Faculty Profiles - ODA, Masahiro

Papers - ODA, Masahiro

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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

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

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

A Multi-scale Pyramid of 3D Fully Convolutional Networks for Abdominal Multi-organ Segmentation Reviewed

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

MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2018, PT IV Vol. 11073 page： 417 - 425 2018

Fully Convolutional Network-Based Eyeball Segmentation from Sparse Annotation for Eye Surgery Simulation Model Reviewed

Sugino Takaaki, Roth Holger R., Oda Masahiro, Mori Kensaku

International Workshop on Bio-Imaging and Visualization for Patient-Customized Simulations, BIVPCS 2018 Vol. 11042 page： 118-126 2018

Fine Segmentation of Tiny Blood Vessel Based on Fully-Connected Conditional Random Field Reviewed

Wang Chenglong, Oda Masahiro, Yoshino Yasushi, Yamamoto Tokunori, Mori Kensaku

MEDICAL IMAGING 2018: IMAGE PROCESSING Vol. 10574 2018

Dense volumetric detection and segmentation of mediastinal lymph nodes in chest CT images Reviewed

Oda Hirohisa, Roth Holger R., Bhatia Kanwal K., Oda Masahiro, Kitasaka Takayuki, Iwano Shingo, Homma Hirotoshi, Takabatake Hirotsugu, Mori Masaki, Natori Hiroshi, Schnabel Julia A., Mori Kensaku

MEDICAL IMAGING 2018: COMPUTER-AIDED DIAGNOSIS Vol. 10575 2018

Deep Learning and Its Application to Medical Image Segmentation Invited

ROTH Holger R., SHEN Chen, ODA Hirohisa, ODA Masahiro, HAYASHI Yuichiro, MISAWA Kazunari, MORI Kensaku

Medical Imaging Technology Vol. 36 ( 2 ) page： 63-71 2018

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

One of the most common tasks in medical imaging is semantic segmentation. Achieving this segmentation automatically has been an active area of research, but the task has been proven very challenging due to the large variation of anatomy across different patients. However, recent advances in deep learning have made it possible to significantly improve the performance of image recognition and semantic segmentation methods in the field of computer vision. Due to the data driven approaches of hierarchical feature learning in deep learning frameworks, these advances can be translated to medical images without much difficulty. Several variations of deep convolutional neural networks have been successfully applied to medical images. Especially fully convolutional architectures have been proven efficient for segmentation of 3D medical images. In this article, we describe how to build a 3D fully convolutional network (FCN) that can process 3D images in order to produce automatic semantic segmentations. The model is trained and evaluated on a clinical computed tomography (CT) dataset and shows stateof-the-art performance in multi-organ segmentation.

DOI： 10.11409/mit.36.63

Cascade classification of endocytoscopic images of colorectal lesions for automated pathological diagnosis Reviewed

Itoh Hayato, Mori Yuichi, Misawa Masashi, Oda Masahiro, Kudo Shin-ei, Mori Kensaku

MEDICAL IMAGING 2018: COMPUTER-AIDED DIAGNOSIS Vol. 10575 2018

BESNet: Boundary-Enhanced Segmentation of Cells in Histopathological Images Reviewed

Oda Hirohisa, Roth Holger R., Chiba Kosuke, Sokolic Jure, Kitasaka Takayuki, Oda Masahiro, Hinoki Akinari, Uchida Hiroo, Schnabel Julia A., Mori Kensaku

MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2018, PT II Vol. 11071 page： 228-236 2018

Automatic segmentation of eyeball structures from micro-CT images based on sparse annotation Reviewed

Sugino Takaaki, Roth Holger R., Oda Masahiro, Omata Seiji, Sakuma Shinya, Arai Fumihito, Mori Kensaku

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

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Papers - ODA, Masahiro