Faculty Profiles - ODA, Masahiro

Presentations -

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A Method for Reducing Distortions on Virtual Stretched Views of the Colon

Development of a Navigation-Based CAD System for Colon International conference

8th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2005)

A Study on Method of Distortion Reduction of Virtual Stretched Image Using Spring Model

2005 Tokai-Section Joint Conference of the Institutes of Electrical and Related Engineers

Development of Computer Aided Diagnosis System for Colorectal Cancer using Virtual Stretched View

仮想展開像と仮想内視鏡像を用いた大腸がん診断支援システムの開発

小田昌宏, 林雄一郎, 北坂孝幸, 森健策, 末永康仁

平成16年度日本エム・イー学会東海支部学術集会

Development of Computer Aided Diagnosis System for Colorectal Cancer based on Navigation Diagnosis

Deep Learning Sample Code Collection for Medical Image Processing: DMED International conference

Masahiro Oda, Takeshi Hara, Kensaku Mori

The 27th Annual Congress of Japan Society of Computer Aided Surgery 2018.11.9

Language：Japanese Presentation type：Poster presentation

Deep learning techniques are widely used in both of computer aided surgery (CAS) and diagnosis (CAD) fields. Many software to use deep learning are available. However, in the medical assisting research field, we need to implement new deep learning-based methods. It is difficult for researchers who are not specialized in image processing or machine learning. To accelerate use of deep learning for all researchers, we developed the deep learning sample code collection for medical image processing (DMED). DMED enables easy use of key deep learning-based methods including medical image processing. Current DMED includes sample codes for image classification, segmentation, and superresolution. Different from the previous deep learning library such as the NiftyNet and the DLTK, the DMED performs training processes in reasonable processing time without GPU. We expect the DMED accelerates use of deep learning-based image processing methods in the CAS and CAD fields.

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医用画像における深層学習を利用した研究のはじめかた Invited International conference

小田昌宏

第37回日本医用画像工学大会 2018.7.25 日本医用画像工学会

Non-contrast to contrasted abdominal CT volume regression using fully convolutional network

Masahiro Oda, Kanako K. Kumamaru, Shigeki Aoki, Kensaku Mori

Computer Assisted Radiology and Surgery (CARS) 2019 2019.6.18

Report on MICCAI 2018 International conference

Masahiro Oda, Yoshito Otake, Hayato Itoh, Takaaki Sugino, Atsushi Saito, Ryo Furukawa, Takashi Onishi, Atsushi Imiya, Kensaku Mori

2019.1.23

MICCAI 2016参加報告 International conference

小田昌宏, 宮内翔子, 諸岡健一, 周向栄, 増谷佳孝, 中口俊哉, 井宮淳, 森健策

電子情報通信学会医用画像研究会 2017.1.18

Machine Learning Techniques for Automated Accurate Organ Segmentation and Their Applications to Diagnosis Assistance

Masahiro Oda, Natsuki Shimizu, Holger R. Roth, Takayuki Kitasaka, Kazunari Misawa, Kensaku Mori, Michitaka Fujiwara, Daniel Rueckert

RSNA 2017 (Radiological Society of North America) 2017.11.26

Hands-on Seminar on JAMIT International conference

Norio Nanata, Takeshi Hara, Masahiro Oda, Daisuke Fukuoka

The 37th JAMIT Annual Meeting 2018.7.25

JAMITハンズオンセミナー International conference

原武史, 中田典生, 小田昌宏, 福岡大輔, 田中理恵, 中山良平

第38回日本医用画像工学会大会 2019.7.24

Generative Adversarial Networks Showcase: Their Mechanisms and Radiological Applications

Masahiro Oda, Hirohisa Oda, Kanako K. Kumamaru, Shigeki Aoki, Hiroshi Natori, Kensaku Mori, Masaki Mori, Hirotsugu Takabatake

Radiological Society of North America (RSNA) 2019 2019.12.1

Estimation of Contrasted Abdominal CT Volume from Non-contrasted CT Volume using Generative Adversarial Frameworks International conference

Masahiro Oda, Kanako Kumamaru, Shigeki Aoki, Kensaku Mori

The 38th JAMIT Annual Meeting 2019.7.24

Language：Japanese Presentation type：Poster presentation

Venue：Nara Kasugano International Forum

We propose an estimation method of an abdominal contrasted CT volume from a non-contrasted CT volume. In diagnosis and treatment, contrasted CT volumes are taken to confirm anatomical structure and abnormal regions related to the blood vessels. However, administration of contrast agents causes dyspnea and cardiac arrest in some cases. In this paper, we propose an estimation method of contrasted images from non-contrasted images in abdominal CT volumes. As the estimation method, we employ an image regression model using a fully convolutional network (FCN). We trained the FCN in a direct-training and indirect-trainings using generative adversarial frameworks (pix2pix, CycleGAN). In our experiments using abdominal CT volumes, contrasted CT-like volumes having good-quality were generated when we used the CycleGAN.

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Fully convolutional networkを用いた小構造物セグメンテーション方法の検討及び腹部動脈への適用 International conference

小田昌宏, Holger R. Roth, 北坂孝幸, 三澤一成, 藤原道隆, 森健策

第37回日本医用画像工学大会（JAMIT2018) 2018.7.25

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

Masahiro Oda, Tokunori Yamamoto, Yasushi Yoshino, Kensaku Mori

Computer Assisted Radiology and Surgery (CARS) 2017 2017.6.20

Extraction of membrane structure in eyeball from MR volumes

Masahiro Oda, Kin Taichi, Kensaku Mori

SPIE Medical Imaging, 2017 2017.2.11

Endocytoscope image classification using deep convolutional neural networks

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

Computer Assisted Radiology and Surgery (CARS) 2017 2017.6.20

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Presentations -