Faculty Profiles - ODA, Masahiro

Papers - ODA, Masahiro

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森悠一, 工藤進英, 三澤将史, 武田健一, 前田康晴, 小川悠史, 一政克朗, 若村邦彦, 林武雅, 工藤豊樹, 宮地英行, 馬場俊之, 伊東隼人, 小田昌宏, 森健策

日本大腸検査学会雑誌 Vol. 36 ( 2 ) page： 77 - 82 2020.5

Language：Japanese Publisher：(社)日本大腸検査学会

EndoBRAIN(製造:サイバーネットシステム株式会社・販売:オリンパス株式会社)は、人工知能による診断支援システムとして、本邦初の薬機法承認を得た医療機器である。EndoBRAINは超拡大内視鏡(CF-H290ECI、オリンパス株式会社)によって取得される520倍の拡大画像をAIが解析することで、大腸病変が腫瘍なのかどうかを瞬時に予測し、医師に情報提供を行う。2012年から開始したEndoBRAINの研究開発は、昭和大学-名古屋大学-サイバーネットシステム株式会社での医工産連携のもと、日本医療研究開発機構(AMED)からのサポート下に継続的に実施され、2018年12月に薬機法承認を取得した。EndoBRAINの臨床的有用性については、2018年に791症例を対象とした大規模前向き試験の結果が公表されており、感度92.7%で腫瘍の鑑別が可能であると報告されている。また、最近公表された前向き試験の副次解析結果によると、EndoBRAINを使用することで、本来切除すべきでない非腫瘍性ポリープの治療件数を抑制することができるため、これにより最大164億円/年の医療費が削減しうることが分かってきた。本稿では、EndoBRAINの特徴・使用法、および薬機法承認取得までの経緯を紹介するとともに、その臨床的有用性および期待される医療費抑制効果について、最新の研究成果を総括する。(著者抄録)

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AIによるSSA/Pの超拡大内視鏡診断

小川悠史, 工藤進英, 森悠一, 三澤将史, 片岡伸一, 前田康晴, 一政克朗, 石垣智之, 工藤豊樹, 若村邦彦, 林武雅, 馬場俊之, 石田文生, 伊東隼人, 小田昌宏, 森健策

日本大腸検査学会雑誌 Vol. 36 ( 2 ) page： 125 - 125 2020.5

Super-resolution of clinical CT volumes with modified CycleGAN using micro CT volumes

Tong ZHENG, Hirohisa ODA, Takayasu MORIYA, Takaaki SUGINO, Shota NAKAMURA, Masahiro ODA, Masaki MORI, Hirotsugu TAKABATAKE, Hiroshi NATORI, Kensaku MORI

CoRR Vol. abs/2004.03272 page： 454 - 459 2020.4

Language：English Publishing type：Research paper (scientific journal) Publisher：日本医用画像工学会

This paper presents a super-resolution (SR) method with unpaired training
dataset of clinical CT and micro CT volumes. For obtaining very detailed
information such as cancer invasion from pre-operative clinical CT volumes of
lung cancer patients, SR of clinical CT volumes to $\m$}CT level is desired.
While most SR methods require paired low- and high- resolution images for
training, it is infeasible to obtain paired clinical CT and {\mu}CT volumes. We
propose a SR approach based on CycleGAN, which could perform SR on clinical CT
into $\mu$CT level. We proposed new loss functions to keep cycle consistency,
while training without paired volumes. Experimental results demonstrated that
our proposed method successfully performed SR of clinical CT volume of lung
cancer patients into $\mu$CT level.

arXiv

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Other Link： http://arxiv.org/pdf/2004.03272v1

How Far Will Clinical Application of AI Applications Advance for Colorectal Cancer Diagnosis? Reviewed

Mori Yuichi, Kudo Shin-ei, Misawa Masashi, Takeda Kenichi, Kudo Toyoki, Itoh Hayato, Oda Masahiro, Mori Kensaku

JOURNAL OF THE ANUS RECTUM AND COLON Vol. 4 ( 2 ) page： 47 - 50 2020.4

Artificial intelligence for magnifying endoscopy, endocytoscopy, and confocal laser endomicroscopy of the colorectum

Mori Yuichi, Kudo Shin-ei, Misawa Masashi, Itoh Hayato, Oda Masahiro, Mori Kensaku

TECHNIQUES AND INNOVATIONS IN GASTROINTESTINAL ENDOSCOPY Vol. 22 ( 2 ) page： 56 - 60 2020.4

Language：Japanese Publisher：Techniques and Innovations in Gastrointestinal Endoscopy

Because magnifying endoscopy is considered to be more accurate at predicting the histology of colorectal polyps than nonmagnifying endoscopy, it has been attracting a lot of attention, especially in Japan. However, use of magnifying endoscopy is not yet widespread because of its limited availability and the difficulty in interpreting the acquired images. Application of artificial intelligence (AI) is now changing this situation because it helps less-skilled endoscopists to accurately interpret magnified images. Research in this field initially focused on magnifying endoscopy with narrow-band imaging as the target of AI. Most previously published retrospective studies have reported over 90% sensitivity in differentiation of neoplastic lesions; however, automatically indicating the region of interest (ROI) of the polyps that AI should analyze has been found to be challenging. To address this practical problem, some researchers have started to adopt contact endomicroscopy as a target for AI. Contact endomicroscopy includes endocytoscopy (520-fold magnification, Olympus, Tokyo, Japan) and confocal laser endomicroscopy (1000-fold magnification, Mauna Kea, Paris, France). These forms of contact endomicroscopy provide ultramagnified images that make it unnecessary to manually select the ROI because the entire image acquired by contact endomicroscopy is the ROI of the targeted polyps. This strength of contact endomicroscopy has contributed to early implementation of this technology into clinical practice, which may change the utility of magnifying endoscopy in clinical settings and help increase its use globally in the near future.

DOI： 10.1016/j.tgie.2019.150632

Web of Science

Scopus

Cardiac fiber tracking on super high-resolution CT images: a comparative study Reviewed

Hirohisa Oda, Holger R. Roth, Takaaki Sugino, Naoki Sunaguchi, Noriko Usami, Masahiro Oda, Daisuke Shimao, Shu Ichihara, Tetsuya Yuasa, Masami Ando, Toshiaki Akita, Yuji Narita, Kensaku Mori

JOURNAL OF MEDICAL IMAGING Vol. 7 ( 2 ) page： 026001 2020.3

粒子線治療の線量測定のためU-Netを使用した水の発光画像からの線量分布推定(Estimation of Dose Distributions from the Luminescence Image of Water Using U-Net for Particle Therapy Dosimetry)

Yabe Takuya, Oda Masahiro, Mori Kensaku, Akagi Takashi, Yamamoto Seiichi

日本放射線技術学会総会学術大会予稿集 Vol. 76回 page： 336 - 337 2020.3

Tensor-cut: A tensor-based graph-cut blood vessel segmentation method and its application to renal artery segmentation Reviewed

Chenglong Wang, Masahiro Oda, Yuichiro Hayashi, Yasushi Yoshino, Tokunori Yamamoto, Alejandro F. Frangi, Kensaku Mori

MEDICAL IMAGE ANALYSIS Vol. 60 page： 101623 2020.2

Micro-computed tomography images of lung adenocarcinoma: detection of lepidic growth patterns Reviewed

Nakamura Shota, Mori Kensaku, Iwano Shingo, Kawaguchi Koji, Fukui Takayuki, Hakiri Shuhei, Ozeki Naoki, Oda Masahiro, Yokoi Kohei

NAGOYA JOURNAL OF MEDICAL SCIENCE Vol. 82 ( 1 ) page： 25 - 31 2020.2

Organ Segmentation From Full-size CT Images Using Memory-Efficient FCN Reviewed

Wang Chenglong, Oda Masahiro, Mori Kensaku

MEDICAL IMAGING 2020: COMPUTER-AIDED DIAGNOSIS Vol. 11314 2020

Usefulness of fine-tuning for deep learning based multi-organ regions segmentation method from non-contrast CT volumes using small training dataset Reviewed

Hayashi Yuichiro, Shen Chen, Roth Holger R., Oda Masahiro, Misawa Kazunari, Jinzaki Masahiro, Hashimoto Masahiro, Kumamaru Kanako K., Aoki Shigeki, Mori Kensaku

MEDICAL IMAGING 2020: COMPUTER-AIDED DIAGNOSIS Vol. 11314 2020

Visualising decision-reasoning regions in computer-aided pathological pattern diagnosis of endoscytoscopic images based on CNN weights analysis Reviewed

Itoh Hayato, Lu Zhongyang, Mori Yuichi, Misawa Masashi, Oda Masahiro, Kudo Shin-ei, Mori Kensaku

MEDICAL IMAGING 2020: COMPUTER-AIDED DIAGNOSIS Vol. 11314 2020

Visualizing intestines for diagnostic assistance of ileus based on intestinal region segmentation from 3D CT images Reviewed

Oda Hirohisa, Nishio Kohei, Kitasaka Takayuki, Amano Hizuru, Takimoto Aitaro, Uchida Hiroo, Suzuki Kojiro, Itoh Hayato, Oda Masahiro, Mori Kensaku

MEDICAL IMAGING 2020: COMPUTER-AIDED DIAGNOSIS Vol. 11314 2020

Deep Learning in Surgical Assistance Invited

Oda Masahiro

Journal of Japan Society of Computer Aided Surgery Vol. 22 ( 1 ) page： 54-58 2020

Clinical Application of EndoBRAIN, a Medical Device Adopting Artificial Intelligence: Challenges to Obtain Regulatory Approval and Insurance Reimbursement Invited

MORI Yuichi, KUDO Shin-ei, MISAWA Masashi, ITOH Hayato, ODA Masahiro, MORI Kensaku

Medical Imaging Technology Vol. 38 ( 5 ) page： 213-216 2020

Language：English Publishing type：Research paper (scientific journal)

<p>EndoBRAIN (Cybernet System Corp.; Olympus Corp., Tokyo) is the computer-aided diagnostic system supportedby artificial intelligence (AI) that allows real-time identification of colorectal polyps. EndoBRAIN was the firstly approved medical device in the field of AI in Japan. This regulatory approval was achieved by the close collaboration among medical researchers, engineering researchers, industrial partners, and public funding bodies. This review article shows the overview of EndoBRAIN including its design, performance, and results in clinical trials. In addition, our approaches to regulatory approval and insurance reimbursement, which unfortunately has not been granted, are also introduced.</p>

DOI： 10.11409/mit.38.213

Spatial information-embedded fully convolutional networks for multi-organ segmentation with improved data augmentation and instance normalization Reviewed

Chen Shen, Chenglong Wang, Holger R. Roth, Masahiro Oda, Yuichiro Hayashi, Kazunari Misawa, Kensaku Mori

Progress in Biomedical Optics and Imaging - Proceedings of SPIE Vol. 11313 2020

Multi-modality super-resolution loss for GAN-based super-resolution of clinical CT images using micro CT image database Reviewed

Tong Zheng, Hirohisa Oda, Takayasu Moriya, Takaaki Sugino, Shota Nakamura, Masahiro Oda, Masaki Mori, Hirotsugu Takabatake, Hiroshi Natori, Kensaku Mori

Progress in Biomedical Optics and Imaging - Proceedings of SPIE Vol. 11313 2020

Improved visual SLAM for bronchoscope tracking and registration with pre-operative CT images Reviewed

Cheng Wang, Masahiro Oda, Yuichiro Hayashi, Takayuki Kitasaka, Hirotoshi Honma, Hirotsugu Takabatake, Masaki Mori, Hiroshi Natori, Kensaku Mori

Proceedings of SPIE - The International Society for Optical Engineering Vol. 11315 2020

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

Oda Masahiro, Yamaguchi Takefumi, Fukuoka Hideki, Ueno Yuta, Mori Kensaku

MEDICAL IMAGING 2020: COMPUTER-AIDED DIAGNOSIS Vol. 11314 2020

Abdominal artery segmentation method from CT volumes using fully convolutional neural network Reviewed

Masahiro Oda, Holger R. Roth, Takayuki Kitasaka, Kazunari Misawa, Michitaka Fujiwara, Kensaku Mori

International Journal of Computer Assisted Radiology and Surgery Vol. 14 ( 12 ) page： 2069 - 2081 2019.12

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Papers - ODA, Masahiro