Faculty Profiles - ODA, Masahiro

Presentations -

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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

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

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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