Faculty Profiles - ODA, Masahiro

Papers - ODA, Masahiro

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Binary polyp-size classification based on deep-learned spatial information Reviewed

Itoh Hayato, Oda Masahiro, Jiang Kai, Mori Yuichi, Misawa Masashi, Kudo Shin-Ei, Imai Kenichiro, Ito Sayo, Hotta Kinichi, Mori Kensaku

International Journal of Computer Assisted Radiology and Surgery Vol. 16 page： 1817 - 1828 2021.9

Depth-based branching level estimation for bronchoscopic navigation Reviewed

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

International Journal of Computer Assisted Radiology and Surgery Vol. 16 ( 10 ) page： 1795 - 1804 2021.8

Deep learning-based medical image processing and their applications beyond image processing Invited

Oda Masahiro

Transactions of Japanese Society for Medical and Biological Engineering Vol. 59 ( 0 ) page： 256 - 256 2021.6

Authorship：Lead author Language：Japanese Publishing type：Research paper (conference, symposium, etc.) Publisher：Japanese Society for Medical and Biological Engineering

Deep learning became an essential method of medical image processing. Deep learning solved many tasks among medical image processing, including image classification, detection, and segmentation. Many deep learning applications were proposed for eye-fundus images and radiological images among medical image modalities. Practical performances were achieved from such applications. In this presentation, we review deep learning-based medical image processing methods and their applications from their methods and image modalities. Also, we introduce new applications of deep learning in medical image processing. Deep learning-based methods process many kinds of data as their explanatory and objective variables. They can be applied to medical data, including image, text, and sensor data. We introduce some examples of such applications. We review new deep learning applications in medical image processing from the examples.

DOI： 10.11239/jsmbe.Annual59.256

Artificial intelligence-assisted colonic endocytoscopy for cancer recognition: a multicenter study Reviewed

Yuichi Mori , Shin-ei Kudo , Masashi Misawa , Kinichi Hotta , Ohtsuka Kazuo , Shoichi Saito , Hiroaki Ikematsu , Yutaka Saito , Takahisa Matsuda , Takeda Kenichi , Toyoki Kudo , Tetsuo Nemoto , Hayato Itoh , Kensaku Mori

Endoscopy International Open page： E1004 - E1011 2021.6

Unsupervised colonoscopic depth estimation by domain translations with a Lambertian-reflection keeping auxiliary task Reviewed

Itoh Hayato, Oda Masahiro, Mori Yuichi, Misawa Masashi, Kudo Shin-Ei, Imai Kenichiro, Ito Sayo, Hotta Kinichi, Takabatake Hirotsugu, Mori Masaki, Natori Hiroshi, Mori Kensaku

International Journal of Computer Assisted Radiology and Surgery Vol. 16 ( 6 ) page： 989 - 1001 2021.6

Deep learning system for automatic detection of bladder tumors in cystoscopic images Reviewed

Mutaguchi J., Oda M., Ueda S., Kinoshita F., Naganuma H., Matsumoto T., Lee K., Monji K., Kashiwagi K., Takeuchi A., Shiota M., Inokuchi J., Mori K., Eto M.

EUROPEAN UROLOGY Vol. 79 page： S1022 - S1023 2021.6

COVID-19診断支援AI開発における名古屋大学の取り組み Invited

小田昌宏, 鄭通, 林雄一郎, 森健策

Medical Imaging Technology Vol. 39 ( 1 ) page： 13 - 19 2021.2

Extremely imbalanced Subarachnoid Hemorrhage detection based on DenseNet-LSTM network with Class-Balanced Loss and Transfer Learning Reviewed

Lu Zhongyang, Oda Masahiro, Hayashi Yuichiro, Hu Tao, Itoh Hayato, Watadani Takeyuki, Abe Osamu, Hashimoto Masahiro, Jinzaki Masahiro, Mori Kensaku

MEDICAL IMAGING 2021: COMPUTER-AIDED DIAGNOSIS Vol. 11597 2021.2

Automated Detection of Spinal Schwannomas Utilizing Deep Learning Based on Object Detection from MRI Reviewed

Sadayuki Ito, Kei Ando, Kazuyoshi Kobayashi, Hiroaki Nakashima Masahiro Oda, Masaaki Machino, Shunsuke Kanbara, Taro Inoue, Hidetoshi Yamaguchi, Hiroyuki Koshimizu, Kensaku Mori, Naoki Ishiguro, Shiro Imagama

Spine Vol. 46 ( 2 ) page： 95 - 100 2021.1

Current status and future perspective on artificial intelligence for lower endoscopy Reviewed

Misawa Masashi, Kudo Shin-ei, Mori Yuichi, Maeda Yasuharu, Ogawa Yushi, Ichimasa Katsuro, Kudo Toyoki, Wakamura Kunihiko, Hayashi Takemasa, Miyachi Hideyuki, Baba Toshiyuki, Ishida Fumio, Itoh Hayato, Oda Masahiro, Mori Kensaku

DIGESTIVE ENDOSCOPY Vol. 33 ( 2 ) page： 273 - 284 2021.1

医療支援AIの現状と情報横断型AIへの期待 Invited

小田昌宏

日本整形外科学会雑誌 Vol. 95 ( 1 ) page： 3 - 8 2021.1

CURRENT STATUS AND FUTURE PERSPECTIVE ON ARTIFICIAL INTELLIGENCE FOR LOWER ENDOSCOPY Reviewed

MISAWA Masashi, MIYACHI Hideyuki, BABA Toshiyuki, ISHIDA Fumio, ITOH Hayato, ODA Masahiro, MORI Kensaku, KUDO Shin-ei, MORI Yuichi, MAEDA Yasuharu, OGAWA Yushi, ICHIMASA Katsuro, KUDO Toyoki, WAKAMURA Kunihiko, HAYASHI Takemasa

GASTROENTEROLOGICAL ENDOSCOPY Vol. 63 ( 7 ) page： 1402 - 1416 2021.1

Language：English Publishing type：Research paper (scientific journal) Publisher：Japan Gastroenterological Endoscopy Society

The global incidence and mortality rate of colorectal cancer remains high. Colonoscopy is regarded as the gold standard examination for detecting and eradicating neoplastic lesions. However, there are some uncertainties in colonoscopy practice that are related to limitations in human performance. First, approximately one-fourth of colorectal neoplasms are missed on a single colonoscopy. Second, it is still difficult for nonexperts to perform adequately regarding optical biopsy. Third, recording of some quality indicators (e.g. cecal intubation, bowel preparation, and withdrawal speed） which are related to adenoma detection rate, is sometimes incomplete. With recent improvements in machine learning techniques and advances in computer performance, artificial intelligence-assisted computer-aided diagnosis is being increasingly utilized by endoscopists. In particular, the emergence of deep-learning, data-driven machine learning techniques have made the development of computer-aided systems easier than that of conventional machine learning techniques, the former currently being considered the standard artificial intelligence engine of computer-aided diagnosis by colonoscopy. To date, computer-aided detection systems seem to have improved the rate of detection of neoplasms. Additionally, computer-aided characterization systems may have the potential to improve diagnostic accuracy in real-time clinical practice. Furthermore, some artificial intelligence-assisted systems that aim to improve the quality of colonoscopy have been reported. The implementation of computer-aided system clinical practice may provide additional benefits such as helping in educational poorly performing endoscopists and supporting real-time clinical decision-making. In this review, we have focused on computer-aided diagnosis during colonoscopy reported by gastroenterologists and discussed its status, limitations, and future prospects.

DOI： 10.11280/gee.63.1402

Scopus

Report on the First JSCAS AI Challenge Invited

Journal of Japan Society of Computer Aided Surgery Vol. 23 ( 2 ) page： 74 - 75 2021

Object Detection method by deep learning for diagnosing bladder tumors in cystoscopic images

Mutaguchi Jun, Oda Masahiro, Kobayashi Satoshi, Inokuchi Junichi, Mori Kensaku, Eto Masatoshi

Transactions of Japanese Society for Medical and Biological Engineering Vol. 59 ( 0 ) page： 299 - 299 2021

Language：Japanese Publisher：Japanese Society for Medical and Biological Engineering

The overlooking bladder tumors during transurethral resection of bladder tumor (TURBT) causes high intravesical recurrence rate. The conventional White light imaging(WLI) and Narrow-band imaging (NBI) were subjective and poor reproducibility due to depending on doctor's experience and skills. Recently, image recognition using artificial intelligence (AI) has been applied for diagnostic imaging and achieving a good performance result, however the feasibility of cystoscopy with AI is still unknown. In this study, we constructed an object detection system based on the AI architecture. We prospectively obtained cystoscopic images from patients who underwent TURBT, and divided these images as the training data and test data. The sensitivity, specificity and Positive predictive value of WLI and NBI were 84.6%, 76.9% and 90.8%, and 78.1%, 97.1% and 95.8%, respectively. Our system has the possibility to improve detection rates of the bladder tumors during cystoscopy, and might be beneficial to reduce recurrence rate of bladder tumors.

DOI： 10.11239/jsmbe.Annual59.299

An Introduction to Deep Learing in Image Recognition（2）: 　Configuration of GPU Environments and Application to Image Segmentation Invited

HARA Takeshi, ODA Masahiro

Medical Imaging Technology Vol. 39 ( 3 ) page： 124 - 130 2021

Language：Japanese Publishing type：Research paper (scientific journal) Publisher：The Japanese Society of Medical Imaging Technology

In order to set up a practical experiment and research environment for deep learning, it is necessary to build an computing environment using GPU. This is because the calculation speed is several tens of times faster than the calculation using only the CPU. In this course, we will explain how to build a GPU environment and clarify how much the calculation time changes, using image segmentation as an example. At the same time, the method of image segmentation using U-Net model and the construction of the label data are also mentioned.

DOI： 10.11409/mit.39.124

Towards Explainable Artificial Intelligence: Introduction Invited

FUJIYOSHI Hironobu, ODA Masahiro

Medical Imaging Technology Vol. 39 ( 3 ) page： 97 - 98 2021

Robust endocytoscopic image classification based on higher-order symmetric tensor analysis and multi-scale topological statistics Reviewed

Hayato Itoh, Yukitaka Nimura, Yuichi Mori, Masashi Misawa, Shin-Ei Kudo, Kinichi Hotta, Kazuo Ohtsuka, Shoichi Saito, Yutaka Saito, Hiroaki Ikematsu, Yuichiro Hayashi, Masahiro Oda, Kensaku Mori

INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY Vol. 15 ( 12 ) page： 2049 - 2059 2020.12

Synthetic laparoscopic video generation for machine learning-based surgical instrument segmentation from real laparoscopic video and virtual surgical instruments Reviewed

Ozawa Takuya, Hayashi Yuichiro, Oda Hirohisa, Oda Masahiro, Kitasaka Takayuki, Takeshita Nobuyoshi, Ito Masaaki, Mori Kensaku

COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING-IMAGING AND VISUALIZATION 2020.11

大腸鏡画像にYOLO-v3を用いた、穿孔の検出と局在化の比率損失設計に関する予備的研究(Preliminary Study of Loss-Functions Design for Detection and Localization of Perforations with YOLO-v3 in Colonoscopic Images)

蒋凱, 伊東隼人, 小田昌宏, 奥村大志, 森悠一, 三澤将史, 林武雅, 工藤進英, 森健策

日本コンピュータ外科学会誌 Vol. 22 ( 4 ) page： 348 - 349 2020.11

SUN database 大腸ポリープ自動検出器の精度評価に向けた試験用画像

伊東隼人, 三澤将史, 森悠一, 小田昌宏, 工藤進英, 森健策

日本コンピュータ外科学会誌 Vol. 22 ( 4 ) page： 346 - 347 2020.11

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Papers - ODA, Masahiro