Faculty Profiles - ODA, Masahiro

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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 International conference

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(JAMIT2017) 2017.7.27

Colonoscope tracking method based on shape estimation network

Masahiro Oda, Holger R. Roth, Takayuki Kitasaka, Kazuhiro Furukawa, Yoshiki Hirooka, Nassir Navab, Kensaku Mori

SPIE Medical Imaging 2019 2019.2.16

Language：English Presentation type：Oral presentation (general)

Venue：San Diego, California, USA

This paper presents a colonoscope tracking method utilizing a colon shape estimation method. CT colonography is used as a less-invasive colon diagnosis method. If colonic polyps or early-stage cancers are found, they are removed in a colonoscopic examination. In the colonoscopic examination, understanding where the colonoscope running in the colon is difficult. A colonoscope navigation system is necessary to reduce overlooking of polyps. We propose a colonoscope tracking method for navigation systems. Previous colonoscope tracking methods caused large tracking errors because they do not consider deformations of the colon during colonoscope insertions. We utilize the shape estimation network (SEN), which estimates deformed colon shape during colonoscope insertions. The SEN is a neural network containing long short-term memory (LSTM) layer. To perform colon shape estimation suitable to the real clinical situation, we trained the SEN using data obtained during colonoscope operations of physicians. The proposed tracking method performs mapping of the colonoscope tip position to a position in the colon using estimation results of the SEN. We evaluated the proposed method in a phantom study. We conrmed that tracking errors of the proposed method was enough small to perform navigation in the ascending, transverse, and descending colons.

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Automated eye disease classification method from anterior eye image using anatomical structure focused image classification technique International conference

Oda M

Progress in Biomedical Optics and Imaging - Proceedings of SPIE 2020

AIプログラミング基礎編，AIプログラミング実践編 Invited International conference

小田昌宏

えひめAI・IoT研究会技術セミナー 2019.4.6 愛媛県経済労働部産業支援局

AIを活用した画像処理について Invited International conference

小田昌宏

えひめAI・IoT研究会技術セミナー 2019.4.6 愛媛県経済労働部産業支援局

AIによるコンピュータ外科の始め方 - 研究環境構築法 Invited International conference

小田昌宏

第27回日本コンピュータ外科学会大会 2018.11.9 第27回日本コンピュータ外科学会大会

AIによるコンピューター手術支援研究の加速 Invited International conference

小田昌宏

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

Abdominal artery segmentation from CT volumes using fully convolutional network for small artery segmentation

Masahiro Oda, Takayuki Kitasaka, Kazunari Misawa, Michitaka Fujiwara, Kensaku Mori

Computer Assisted Radiology and Surgery (CARS) 2018 2018.6.20

3D FCN Feature Driven Regression Forest-Based Pancreas Localization and Segmentation

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

MICCAI 2017, 3rd Workshop on Deep Learning in Medical Image Analysis 2017.9.14

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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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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仮想腹腔鏡画像生成と深層学習による腹腔鏡画像からの術具領域セグメンテーション

小澤卓也, 林雄一郎, 小田紘久, 小田昌宏, 北坂孝幸, 竹下修由, 伊藤雅昭, 森健策

電子情報通信学会医用画像研究会医用画像研究会 2020.1.29

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