Faculty Profiles - ODA, Masahiro

Papers - ODA, Masahiro

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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

【多元計算解剖学の診断・治療・医工学への展開】人工知能に基づく医療機器EndoBRAINの臨床導入薬事承認取得・保険算定への挑戦

森悠一, 工藤進英, 三澤将史, 伊東隼人, 小田昌宏, 森健策

MEDICAL IMAGING TECHNOLOGY Vol. 38 ( 5 ) page： 213 - 216 2020.11

腹腔鏡動画像用オンラインアノテーションツールの開発

屠芸豪, 伊東隼人, 小澤卓也, 小田昌宏, 竹下修由, 伊藤雅昭, 森健策

日本コンピュータ外科学会誌 Vol. 22 ( 4 ) page： 306 - 307 2020.11

Artificial Intelligence System to Determine Risk of T1 Colorectal Cancer Metastasis to Lymph Node. Reviewed

Kudo SE, Ichimasa K, Villard B, Mori Y, Misawa M, Saito S, Hotta K, Saito Y, Matsuda T, Yamada K, Mitani T, Ohtsuka K, Chino A, Ide D, Imai K, Kishida Y, Nakamura K, Saiki Y, Tanaka M, Hoteya S, Yamashita S, Kinugasa Y, Fukuda M, Kudo T, Miyachi H, Ishida F, Itoh H, Oda M, Mori K

Gastroenterology 2020.9

Prediction of dose distribution from luminescence image of water using a deep convolutional neural network for particle therapy Reviewed

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

MEDICAL PHYSICS Vol. 47 ( 9 ) page： 3882 - 3891 2020.9

大腸内視鏡における穿孔の自動検出および位置推定に関する予備的検討

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

日本医用画像工学会大会予稿集 Vol. 39回 page： 29 - 29 2020.9

位置特徴量とマルチスケール特徴量による胃壁マイクロCT像からの胃壁の層構造及び腫瘍の抽出

御手洗翠, 小田紘久, 杉野貴明, 守谷享泰, 伊東隼人, 小田昌宏, 小宮山琢真, 古川和宏, 宮原良二, 藤城光弘, 森雅樹, 高畠博嗣, 名取博, 森健策

日本医用画像工学会大会予稿集 Vol. 39回 page： 569 - 572 2020.9

Language：Japanese Publisher：日本医用画像工学会

本稿では,Spherical K-means(SpK)により抽出された複数スケールの特徴量と位置特徴量を用いた胃壁μCT画像からの粘膜層,粘膜下層,筋層及び腫瘍の抽出手法について報告する.胃壁のμCT画像から解剖学的構造を抽出することで腫瘍の立体構造把握が可能である.従来手法では複数スケールの特徴抽出フィルタをSpKによって学習し,パッチから特徴抽出を行った.その後得られた特徴量に基づいてSVMによって分類を行っていた.しかし,SpKにより生成された特徴抽出フィルタは濃度値に基づく特徴量を抽出するため,粘膜層と筋層のように濃度値が類似した領域同士の特徴量が近しい値になる.本手法ではSpKにより得られる複数スケールの特徴量にパッチの位置特徴量を追加して分類する事によって,濃度値に基づく特徴量では分類できない領域の分類を可能にする.パッチの位置特徴量としてパッチの中心座標を用いる.本手法を胃壁μCT像に適用した結果,粘膜層,粘膜下層,筋層及び腫瘍の抽出に対するDICE係数は68.3%,63.2%,82.2%,69.1%であった.特に従来手法と比べ,腫瘍の抽出に対するDICE係数は26.1%向上した.(著者抄録)

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A visual SLAM-based bronchoscope tracking scheme for bronchoscopic navigation Reviewed

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

INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY Vol. 15 ( 10 ) page： 1619 - 1630 2020.8

Development of a computer-aided detection system for colonoscopy and a publicly accessible large colonoscopy video database (with video). Reviewed

Misawa M, Kudo SE, Mori Y, Hotta K, Ohtsuka K, Matsuda T, Saito S, Kudo T, Baba T, Ishida F, Itoh H, Oda M, Mori K

Gastrointestinal endoscopy 2020.7

A deformable model for navigated laparoscopic gastrectomy based on finite elemental method Reviewed

Chen Tao, Wei Guodong, Xu Lili, Shi Weili, Xu Yikai, Zhu Yongyi, Hayashi Yuichiro, Oda Hirohisa, Oda Masahiro, Hu Yanfeng, Yu Jiang, Jiang Zhengang, Li Guoxin, Mori Kensaku

MINIMALLY INVASIVE THERAPY & ALLIED TECHNOLOGIES Vol. 29 ( 4 ) page： 210 - 216 2020.7

Artificial Intelligence-assisted System Improves Endoscopic Identification of Colorectal Neoplasms Reviewed

Kudo Shin-ei, Misawa Masashi, Mori Yuichi, Hotta Kinichi, Ohtsuka Kazuo, Ikematsu Hiroaki, Saito Yutaka, Takeda Kenichi, Nakamura Hiroki, Ichimasa Katsuro, Ishigaki Tomoyuki, Toyoshima Naoya, Kudo Toyoki, Hayashi Takemasa, Wakamura Kunihiko, Baba Toshiyuki, Ishida Fumio, Inoue Haruhiro, Itoh Hayato, Oda Masahiro, Mori Kensaku

CLINICAL GASTROENTEROLOGY AND HEPATOLOGY Vol. 18 ( 8 ) page： 1874 - + 2020.7

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Papers - ODA, Masahiro