Faculty Profiles - ODA, Masahiro

Presentations -

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Colonoscope tracking method based on shape estimation network International conference

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

SPIE Medical Imaging 2019

Event date： 2019.2

Language：English Presentation type：Oral presentation (general)

Venue：San Diego, California, USA Country：United States

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.

DOI： 10.1117/12.2512729

Report on MICCAI 2018

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

Deep Learning Sample Code Collection for Medical Image Processing: DMED

Masahiro Oda, Takeshi Hara, Kensaku Mori

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

Event date： 2018.11

Language：Japanese Presentation type：Poster presentation

Country：Japan

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.

Deep learning for medical images in surgical field

Masahiro Oda

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

Event date： 2018.11

Language：Japanese Presentation type：Oral presentation (invited, special)

Country：Japan

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.

AIによるコンピュータ外科の始め方 - 研究環境構築法 Invited

小田昌宏

第27回日本コンピュータ外科学会大会

Colon Shape Estimation Method for Colonoscope Tracking using Recurrent Neural Networks International conference

Masahiro Oda, Holger Roth, Takayuki Kitasaka, Kazuhiro Furukawa, Ryoji Miyahara, Yoshiki Hirooka, Hidemi Goto, Nassir Navab, Kensaku Mori

MICCAI 2018

深層学習を用いた医用画像処理の始め方 Invited

小田昌宏

生体医工学シンポジウム2018

Tutorial of Deep Learning

Masahiro Oda

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

小田昌宏

第37回日本医用画像工学大会

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

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)

Hands-on Seminar on JAMIT

Norio Nanata, Takeshi Hara, Masahiro Oda, Daisuke Fukuoka

The 37th JAMIT Annual Meeting

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

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

Computer Assisted Radiology and Surgery (CARS) 2018

機械学習を用いた診断と治療支援 Invited

小田昌宏

第47回日本脊椎脊髄病学会学術集会

Machine learning-based colon deformation estimation method for colonscope tracking International conference

Masahiro Oda, Takayuki Kitasaka, Kazuhiro Furukawa, Ryoji Miyahara, Yoshiki Hirooka, Hidemi Goto, Nassir Navab, Kensaku Mori

SPIE Medical Imaging

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

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

RSNA 2017 (Radiological Society of North America)

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

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)

AIによるコンピューター手術支援研究の加速 Invited

小田昌宏

第26回日本コンピュータ外科学会大会(JSCAS2017)

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

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

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

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)

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

Masahiro Oda, Tokunori Yamamoto, Yasushi Yoshino, Kensaku Mori

Computer Assisted Radiology and Surgery (CARS) 2017

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