Faculty Profiles - ODA, Masahiro

Papers - ODA, Masahiro

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A Multi-scale Pyramid of 3D Fully Convolutional Networks for Abdominal Multi-organ Segmentation Reviewed

Roth Holger R., Shen Chen, Oda Hirohisa, Sugino Takaaki, Oda Masahiro, Hayashi Yuichiro, Misawa Kazunari, Mori Kensaku

MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2018, PT IV Vol. 11073 page： 417 - 425 2018

Fully Convolutional Network-Based Eyeball Segmentation from Sparse Annotation for Eye Surgery Simulation Model Reviewed

Sugino Takaaki, Roth Holger R., Oda Masahiro, Mori Kensaku

International Workshop on Bio-Imaging and Visualization for Patient-Customized Simulations, BIVPCS 2018 Vol. 11042 page： 118-126 2018

Fine Segmentation of Tiny Blood Vessel Based on Fully-Connected Conditional Random Field Reviewed

Wang Chenglong, Oda Masahiro, Yoshino Yasushi, Yamamoto Tokunori, Mori Kensaku

MEDICAL IMAGING 2018: IMAGE PROCESSING Vol. 10574 2018

Dense volumetric detection and segmentation of mediastinal lymph nodes in chest CT images Reviewed

Oda Hirohisa, Roth Holger R., Bhatia Kanwal K., Oda Masahiro, Kitasaka Takayuki, Iwano Shingo, Homma Hirotoshi, Takabatake Hirotsugu, Mori Masaki, Natori Hiroshi, Schnabel Julia A., Mori Kensaku

MEDICAL IMAGING 2018: COMPUTER-AIDED DIAGNOSIS Vol. 10575 2018

Deep Learning and Its Application to Medical Image Segmentation Invited

ROTH Holger R., SHEN Chen, ODA Hirohisa, ODA Masahiro, HAYASHI Yuichiro, MISAWA Kazunari, MORI Kensaku

Medical Imaging Technology Vol. 36 ( 2 ) page： 63-71 2018

Language：English Publishing type：Research paper (conference, symposium, etc.)

One of the most common tasks in medical imaging is semantic segmentation. Achieving this segmentation automatically has been an active area of research, but the task has been proven very challenging due to the large variation of anatomy across different patients. However, recent advances in deep learning have made it possible to significantly improve the performance of image recognition and semantic segmentation methods in the field of computer vision. Due to the data driven approaches of hierarchical feature learning in deep learning frameworks, these advances can be translated to medical images without much difficulty. Several variations of deep convolutional neural networks have been successfully applied to medical images. Especially fully convolutional architectures have been proven efficient for segmentation of 3D medical images. In this article, we describe how to build a 3D fully convolutional network (FCN) that can process 3D images in order to produce automatic semantic segmentations. The model is trained and evaluated on a clinical computed tomography (CT) dataset and shows stateof-the-art performance in multi-organ segmentation.

DOI： 10.11409/mit.36.63

Cascade classification of endocytoscopic images of colorectal lesions for automated pathological diagnosis Reviewed

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

MEDICAL IMAGING 2018: COMPUTER-AIDED DIAGNOSIS Vol. 10575 2018

BESNet: Boundary-Enhanced Segmentation of Cells in Histopathological Images Reviewed

Oda Hirohisa, Roth Holger R., Chiba Kosuke, Sokolic Jure, Kitasaka Takayuki, Oda Masahiro, Hinoki Akinari, Uchida Hiroo, Schnabel Julia A., Mori Kensaku

MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2018, PT II Vol. 11071 page： 228-236 2018

Automatic segmentation of eyeball structures from micro-CT images based on sparse annotation Reviewed

Sugino Takaaki, Roth Holger R., Oda Masahiro, Omata Seiji, Sakuma Shinya, Arai Fumihito, Mori Kensaku

MEDICAL IMAGING 2018: BIOMEDICAL APPLICATIONS IN MOLECULAR, STRUCTURAL, AND FUNCTIONAL IMAGING Vol. 10578 2018

Airway Segmentation from 3D Chest CT Volumes Based on Volume of Interest Using Gradient Vector Flow

MENG Qier, KITASAKA Takayuki, ODA Masahiro, UENO Junji, MORI Kensaku

Medical Imaging Technology Vol. 36 ( 3 ) page： 133-146 2018

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

In this paper, we propose a new airway segmentation algorithm from 3D chest CT volumes based on the volume of interest (VOI). The algorithm segments each bronchial branch by recognizing the airway regions from the trachea using the VOIs to segment each branch. A VOI is placed to envelop the branch currently being processed. Then a cavity enhancement filter is performed only inside the current VOI so that each branch is extracted. At the same time, we perform a leakage detection scheme to avoid any leakage regions inside the VOI. Next the gradient vector flow magnitude map and a tubular-likeness function are computed in each VOI. This assists the predictions of both the position and direction of the next child VOIs to detect the next child branches to continue the tracking algorithm. Finally, we unify all of the extracted airway regions to form a complete airway tree. We used a dataset that includes 50 standard-dose human chest CT volumes to evaluate our proposed algorithm. The average extraction rate was approximately 78.1% with a significantly decreased false positive rate compared to the previous method.

DOI： 10.11409/mit.36.133

Machine learning-based colon deformation estimation method for colonoscope tracking Reviewed

Oda Masahiro, Kitasaka Takayuki, Furukawa Kazuhiro, Miyahara Ryoji, Hirooka Yoshiki, Goto Hidemi, Navab Nassir, Mori Kensaku

MEDICAL IMAGING 2018: IMAGE-GUIDED PROCEDURES, ROBOTIC INTERVENTIONS, AND MODELING Vol. 10576 2018

Development of a CNC abrasive machining technique for 3D-printed organ models by utilizing a flexible mechanical structure

OHBA Takayuki, SUZUKI Norikazu, SHAMOTO Eiji, ODA Masahiro, MORI Kensaku, TAKEUCHI Yoshimi

The Proceedings of The Manufacturing & Machine Tool Conference Vol. 2018.12 ( 0 ) page： C20 2018

Language：Japanese Publisher：The Japan Society of Mechanical Engineers

This paper presents a novel surface finishing technology for free-formed surfaces of three-dimensional (3D) printed resin organ models. Demand for the medical applications of three-dimensional layered fabrication technology such as organ models is increasing. Surface integrity deteriorations due to laminated micro steps formed on the surface is a significant problem. In general, material removal of layered micro step is performed manually, hence automation technology is desired. In the present study, we proposed a quasi-constant machining force grinding / polishing by use of 3-axis CNC machine tools. The workpiece is fixed to the flexible mechanical structure, whose translational flexibilities are designed to be identical regardless of contact directions and low spring rigidity. With the developed machining system, the liver model processing was demonstrated. Experimental results verified the feasibility of highly-efficient abrasive machining.

DOI： 10.1299/jsmemmt.2018.12.c20

CiNii Research

On the influence of Dice loss function in multi-class organ segmentation of abdominal CT using 3D fully convolutional networks

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

MI2017-51 Vol. 117 ( 281 ) page： 15-20 2017.11

Study on the Robustness of ORB-SLAM Based Outlier Elimination in Bronchoscope Tracking -- RANSAC + EPnP for Outlier Detection --

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

MI2017-58 Vol. 117 ( 281 ) page： 47-52 2017.11

Automated Multi-Organ Segmentation in Abdominal CT with Hierarchical 3D Fully-Convolutional Networks

Holger R. Roth, Hirohisa Oda, MENG, Yuichiro Hayashi, Masahiro Oda, Natsuki Shimizu, Kensaku Mori, Michitaka Fujiwara, Kazunari Misawa

RSNA 2017 (Radiological Society of North America) Scientific Assembly and Annual Meeting PROGRAM IN BRIEF page： 267 2017.11

Automated mediastinal lymph node detection from CT volumes based on intensity targeted radial structure tensor analysis Reviewed International coauthorship

Hirohisa Oda, Kanwal K. Bhatia, Masahiro Oda, Takayuki Kitasaka, Shingo Iwano, Hirotoshi Homma, Hirotsugu Takabatake, Masaki Mori, Hiroshi Natori, Julia A. Schnabel, Kensaku Mori

JOURNAL OF MEDICAL IMAGING Vol. 4 ( 4 ) page： 044502 2017.10

3D Fully Convolutional Networks と全連結条件付確率場による 3 次元 CT 画像からの多臓器自動抽出に関する検討

楊瀛, 小田昌宏, Roth Holger, 北坂孝幸, 三澤一成, 森健策

日本コンピュータ外科学会誌第26回日本コンピュータ外科学会大会特集号 Vol. 19 ( 4 ) page： 268-269 2017.10

μCT 画像を用いた大変形を含む連続切片 HE 染色画像の 3 次元再構築

長柄快, Holger Roth, 中村彰太, 小田紘久, 守谷享泰, 小田昌宏, 森健策

日本コンピュータ外科学会誌第26回日本コンピュータ外科学会大会特集号 Vol. 19 ( 4 ) page： 363-364 2017.10

深層学習を用いたマイクロ CT 画像からの眼球構造自動抽出〜少量データ学習による解剖構造抽出性能の検証

杉野貴明, Holger R. Roth, 小田昌宏, 小俣誠二, 佐久間臣耶, 新井史人, 森健策

日本コンピュータ外科学会誌第26回日本コンピュータ外科学会大会特集号 Vol. 19 ( 4 ) page： 241-242 2017.10

気管支鏡追跡における ORB-SLAM 適用に関する初期的検討

王成, 小田昌宏, 林雄一郎, 森健策

日本コンピュータ外科学会誌第26回日本コンピュータ外科学会大会特集号 Vol. 19 ( 4 ) page： 324-325 2017.10

機械学習を用いた腹部動脈血管名自動命名における臓器情報利用方法に関する一考察

鉄村悠介, Holger Roth, 林雄一郎, 小田昌宏, 進藤幸治, 大内田研宙, 橋爪誠, 三澤一成, 森健策

日本コンピュータ外科学会誌第26回日本コンピュータ外科学会大会特集号 Vol. 19 ( 4 ) page： 361-362 2017.10

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Papers - ODA, Masahiro