Faculty Profiles - MORI, Kensaku

Papers - MORI, Kensaku

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Development of a new laparoscopic detection system for gastric cancer using near-infrared light-emitting clips with glass phosphor Invited Reviewed

Inada S., Nakanishi H., Oda M., Mori K., Ito A., Hasegawa J., Misawa K., Fuchi S.

Micromachines Vol. 10 ( 2 ) 2019.1

Authorship：Corresponding author Language：Japanese Publishing type：Research paper (scientific journal) Publisher：Micromachines

Laparoscopic surgery is now a standard treatment for gastric cancer. Currently, the location of the gastric cancer is identified during laparoscopic surgery via the preoperative endoscopic injection of charcoal ink around the primary tumor; however, the wide spread of injected charcoal ink can make it difficult to accurately visualize the specific site of the tumor. To precisely identify the locations of gastric tumors, we developed a fluorescent detection system comprising clips with glass phosphor (Yb 3+ , Nd 3+ doped to Bi 2 O 3 -B 2 O 3 -based glasses, size: 2 mm × 1 mm × 3 mm) fixed in the stomach and a laparoscopic fluorescent detection system for clip-derived near-infrared (NIR) light (976 nm). We conducted two ex vivo experiments to evaluate the performance of this fluorescent detection system in an extirpated pig stomach and a freshly resected human stomach and were able to successfully detect NIR fluorescence emitted from the clip in the stomach through the stomach wall by the irradiation of excitation light (λ: 808 nm). These results suggest that the proposed combined NIR light-emitting clip and laparoscopic fluorescent detection system could be very useful in clinical practice for accurately identifying the location of a primary gastric tumor during laparoscopic surgery.

DOI： 10.3390/mi10020081

Scopus

Automated Hand Eye Calibration in Laparoscope Holding Robot for Robot Assisted Surgery Invited Reviewed

Jiang Shuai, Hayashi Yuichiro, Wang Cheng, Oda Masahiro, Kitasaka Takayuki, Misawa Kazunari, Mori Kensaku

INTERNATIONAL WORKSHOP ON ADVANCED IMAGE TECHNOLOGY (IWAIT) 2019 Vol. 11049 2019

Colonoscope tracking method based on shape estimation network Invited Reviewed

Oda Masahiro, Roth Holger R., Kitasaka Takayuki, Furukawa Kazuhiro, Miyahara Ryoji, Hirooka Yoshiki, Navab Nassir, Mori Kensaku

MEDICAL IMAGING 2019: IMAGE-GUIDED PROCEDURES, ROBOTIC INTERVENTIONS, AND MODELING Vol. 10951 2019

Authorship：Corresponding author Language：Japanese Publishing type：Research paper (scientific journal) Publisher：Progress in Biomedical Optics and Imaging - Proceedings of SPIE

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 confirmed that tracking errors of the proposed method was enough small to perform navigation in the ascending, transverse, and descending colons.

DOI： 10.1117/12.2512729

Web of Science

Scopus

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

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

Proc. SPIE 10578, Medical Imaging 2018 Vol. 10578 page： 105780V-1-105780V-6 2018

Cascade classification of lesions for automated pathological diagnosis Invited Reviewed

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

Proc. SPIE 10575, Medical Imaging 2018 Vol. 10575 page： 1057516-1-1057516-6 2018

3Dプリンタの最新動向 Invited

森健策

インナービジョン Vol. 33 page： 35 - 36 2018

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

Develop and Validate a Finite Element Method Model for Deformation Matching of Laparoscopic Gastrectomy Navigation Invited Reviewed

Chen Tao, Wei Guodong, Shi Weili, Hayashi Yuichiro, Oda Masahiro, Jiang Zhengang, Li Guoxin, Mori Kensaku

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

Deep Learning and Its Application to Medical Image Segmentation Invited Reviewed

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

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

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

CiNii Research

Colon Shape Estimation Method for Colonoscope Tracking Using Recurrent Neural Networks Invited Reviewed

Oda Masahiro, Roth Holger R., Kitasaka Takayuki, Furukawa Kasuhiro, Miyahara Ryoji, Hirooka Yoshiki, Goto Hidemi, Navab Nassir, Mori Kensaku

MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2018, PT IV Vol. 11073 page： 176 - 184 2018

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

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

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

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

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

CiNii Research

<b>3Dプリンターの基礎と医療応用</b> Invited Reviewed

森健策

心臓 Vol. 49 ( 11 ) page： 1104 - 1113 2017.11

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

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 ( 04 ) page： 1 2017.11

Automatic segmentation of head anatomical structures from sparsely-Annotated images Invited Reviewed

Sugino T., Roth H.R., Eshghi M., Oda M., Chung M.S., Mori K.

2017 IEEE International Conference on Cyborg and Bionic Systems, CBS 2017 Vol. 2018-January page： 145 - 149 2017.7

Authorship：Corresponding author Language：English Publishing type：Research paper (scientific journal) Publisher：2017 IEEE International Conference on Cyborg and Bionic Systems, CBS 2017

Bionic humanoid systems, which are elaborate human models with sensors, have been developed as a tool for quantitative evaluation of doctors' psychomotor skills and medical device performances. For creation of the elaborate human models, this study presents automated segmentation of head sectioned images using sparsely-Annotated data based on deep convolutional neural network. We applied the following fully convolutional networks (FCNs) to the sparse-Annotation-based segmentation: a standard FCN and a dilated convolution based FCN. To validate the availability of FCNs for segmentation of head structures from sparse annotation, we performed 8- and 243-label segmentation experiments using different two sets of head sectioned images in the Visible Korean Human project. In the segmentation experiments, only 10% of all images in each data set were used for training data. Both of the FCNs could achieve the mean segmentation accuracy of more than 85% in the 8-label segmentation. In the 243-label segmentation, though the mean segmentation accuracy was about 50%, the results suggested that the FCNs, especially the dilated convolution based FCNs, had potential to achieve accurate segmentation of anatomical structures, except for small-sized and complex-shaped tissues, even from sparse annotation.

DOI： 10.1109/CBS.2017.8266085

Scopus

Artificial Intelligence for Endocytoscopy Provides Fully Automated Diagnosis of Histological Remission in Ulcerativ E. Coli Tis Invited Reviewed

Yasuharu Maeda, Kudo Shinei, Mori Yuichi, Misawa Masashi, Wakamura Kunihiko, Hayashi Seiko, Ogata Noriyuki, Takeda Kenichi, Kudo Toyoki, Hayashi Takemasa, Katagiri Atsushi, Ishida Fumio, Ohtsuka Kazuo, Oda Masahiro, Mori Kensaku

GASTROINTESTINAL ENDOSCOPY Vol. 85 ( 5 ) page： AB248 - AB248 2017.5

Can Artificial Intelligence Correctly Diagnose Sessile Serrated Adenomas/Polyps? Invited Reviewed

Mori Yuichi, Kudo Shinei, Ogawa Yushi, Misawa Masashi, Takeda Kenichi, Kudo Toyoki, Wakamura Kunihiko, Hayashi Takemasa, Ichimasa Katsuro, Maeda Yasuharu, Toyoshima Naoya, Nakamura Hiroki, Katagiri Atsushi, Baba Toshiyuki, Ishida Fumio, Oda Masahiro, Mori Kensaku, Inoue Haruhiro

GASTROINTESTINAL ENDOSCOPY Vol. 85 ( 5 ) page： AB510 - AB510 2017.5

Diagnostic Ability of Automated Diagnosis System Using Endocytoscopy for Invasive Colorectal Cancer Invited Reviewed

Takeda Kenichi, Kudo Shinei, Mori Yuichi, Kataoka Shinichi, Yasuharu Maeda, Ogawa Yushi, Nakamura Hiroki, Misawa Masashi, Kudo Toyoki, Wakamura Kunihiko, Hayashi Takemasa, Katagiri Atsushi, Baba Toshiyuki, Hidaka Eiji, Ishida Fumio, Inoue Haruhiro, Oda Masahiro, Mori Kensaku

GASTROINTESTINAL ENDOSCOPY Vol. 85 ( 5 ) page： AB408 - AB408 2017.5

Computer-Aided Diagnosis Based on Endocytoscopy With Narrow-Band Imaging Allows Accurate Diagnosis of Diminutive Colorectal Lesions Invited Reviewed

Misawa Masashi, Kudo Shinei, Mori Yuichi, Takeda Kenichi, Kataoka Shinichi, Nakamura Hiroki, Maeda Yasuharu, Ogawa Yushi, Yamauchi Akihiro, Igarashi Kenta, Hayashi Takemasa, Kudo Toyoki, Wakamura Kunihiko, Katagiri Atsushi, Baba Toshiyuki, Ishida Fumio, Oda Masahiro, Mori Kensaku

GASTROINTESTINAL ENDOSCOPY Vol. 85 ( 5 ) page： AB57 - AB57 2017.5

Influence of using 3D images and 3D-printed objects on the formation of spatial mental models of experts and novices Invited Reviewed

MAEHIGASHI Akihiro, MIWA Kazuhisa, ODA Masahiro, NAKAMURA Yoshihiko, MORI Kensaku, IGAMI Tsuyoshi

JSAI Technical Report, SIG-ALST Vol. 79 ( 0 ) page： 08 2017.3

3D FCN Feature Driven Regression Forest-Based Pancreas Localization and Segmentation Invited Reviewed

Oda Masahiro, Shimizu Natsuki, Roth Holger R., Karasawa Ken'ichi, Kitasaka Takayuki, Misawa Kazunari, Fujiwara Michitaka, Rueckert Daniel, Mori Kensaku

DEEP LEARNING IN MEDICAL IMAGE ANALYSIS AND MULTIMODAL LEARNING FOR CLINICAL DECISION SUPPORT Vol. 10553 page： 222 - 230 2017

Authorship：Corresponding author Language：English Publishing type：Research paper (scientific journal) Publisher：Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

This paper presents a fully automated atlas-based pancreas segmentation method from CT volumes utilizing 3D fully convolutional network (FCN) feature-based pancreas localization. Segmentation of the pancreas is difficult because it has larger inter-patient spatial variations than other organs. Previous pancreas segmentation methods failed to deal with such variations. We propose a fully automated pancreas segmentation method that contains novel localization and segmentation. Since the pancreas neighbors many other organs, its position and size are strongly related to the positions of the surrounding organs. We estimate the position and the size of the pancreas (localization) from global features by regression forests. As global features, we use intensity differences and 3D FCN deep learned features, which include automatically extracted essential features for segmentation. We chose 3D FCN features from a trained 3D U-Net, which is trained to perform multi-organ segmentation. The global features include both the pancreas and surrounding organ information. After localization, a patient-specific probabilistic atlas-based pancreas segmentation is performed. In evaluation results with 146 CT volumes, we achieved 60.6% of the Jaccard index and 73.9% of the Dice overlap.

DOI： 10.1007/978-3-319-67558-9_26

Web of Science

Scopus

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Papers - MORI, Kensaku