Faculty Profiles - ODA, Masahiro

Presentations -

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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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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

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

名古屋大学スーパーコンピュータ「不老」における医用画像処理

大島聡史, 小田昌宏, 片桐孝洋, 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 2020.9.3

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Presentations -