Faculty Profiles - ODA, Masahiro

Presentations -

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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

Realistic virtual endoscopic image generation method using image domain translation International conference

Masahiro Oda, Kiyohito Tanaka, Hirotsugu, Takabatake, Masaki Mori, Hiroshi Natori, Kensaku Mori

The 28th Annual Congress of Japan Society of Computer Aided Surgery 2019.11.22

Language：Japanese Presentation type：Oral presentation (general)

Venue：University of Tokyo

We propose a realistic virtual endoscopic image generation method for endoscope simulators. Endoscope simulators are used to train endoscopists. However, less realistic visualization of simulators reduces effect of training. Main differences of virtual endoscopic images generated by simulators and real endoscopic images are textures and light reflections on the surface of organs. The differences should be reduced to improve reality of virtual images. This paper proposes a method to improve reality of virtual endoscopic images using a virtual to real endoscopic image domain translation technique. In the image domain translation, the structure of fully convolutional network (FCN) that performs image translation has relation to the reality of translated images. We experimentally confirmed reality of translated images using three different FCNs. From the result, use of larger numbers of kernels and layers resulted in generation of realistic images.

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Tutorial of Deep Learning International conference

Masahiro Oda

2018.9.14

深層学習を用いた医用画像処理研究の最前線 Invited International conference

小田昌宏

第47回日本放射線技術学会秋季学術大会（第86回画像部会教育講演） 2019.10.17 第47回日本放射線技術学会秋季学術大会（第86回画像部会教育講演）

深層学習を用いた医用画像処理の始め方 Invited International conference

小田昌宏

生体医工学シンポジウム2018 2018.9.14 生体医工学シンポジウム2018

機械学習を用いた診断と治療支援 Invited International conference

小田昌宏

第47回日本脊椎脊髄病学会学術集会 2018.4.12 日本脊椎脊髄病学会

大腸変形モデルを用いた大腸内視鏡下治療誘導システムの開発 Invited International conference

小田昌宏

第31回人工知能学会全国大会 2017.5.23 JST

大腸内視鏡トラッキングのためのregression forestsを用いた大腸変形モデルの開発 International conference

小田昌宏, 北坂孝幸, 古川和宏, 宮原良二, 廣岡芳樹, 後藤秀実, Nassir Navabe, 森健策

第26回日本コンピュータ外科学会大会(JSCAS2017) 2017.10.28

Development of AI assisted diagnosis and treatment powered by integrated use of various data Invited International conference

Masahiro Oda

The 92th Annual Meeting of the Japan Orthopaedic Association 2019.5.9 The 92th Annual Meeting of the Japan Orthopaedic Association

Deep learning for medical images in surgical field International conference

Masahiro Oda

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

Language：Japanese Presentation type：Public lecture, seminar, tutorial, course, or other speech

Deep learning contributes growing of the information processing field. It is used to achieve computer systems having high recognition performances. Traditionally, recognition algorithm was individually developed for each task. In contrast to them, a common model or a framework is applicable to many recognition tasks in the deep learning field. This property contributes acceleration of research and development. In this session, we explain how to perform image processing using deep learning in hands-on style. How to develop and execute basic deep learning-based image processing methods including image classification and segmentation are explained. Programs of these methods will run on attendee's computers. Attendee are requested to install software and download data to their own computers prior to joining the hands-on.

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AI and Machine Learning in Medical Image Engineering Invited International conference

Masahiro Oda

The 63rd Annual Conference of the Institute of Systems, Control and Information Engineers (SCI'19) 2019.5.22 The 63rd Annual Conference of the Institute of Systems, Control and Information Engineers (SCI'19)

Language：Japanese Presentation type：Public lecture, seminar, tutorial, course, or other speech

Venue：Central Electric Club

In the medical image processing field, utilization of AI and machine learning techniques are getting popular. Among AI and machine learning techniques, the deep learning significantly contributes to improve performances of medical image processing methods. This paper introduces some automated segmentation and lesion detection techniques including conventional and deep learning-based methods. Improvements of their performances obtained by using deep learning techniques are also presented. Additionally, two problems that medical image processing researches who use AI and machine learning techniques facing are introduced.

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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

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Presentations -