Faculty Profiles - MORI, Kensaku

Papers - MORI, Kensaku

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Feature-based registration of micro CT volumes

Kai Nagara, Shota Nakamura, Hoiger R. Roth, Masahiro Oda, Hirotoshi Homma, Hirotsugu Takabatake, Masaki Mori, Hiroshi Natori, Kesaku Mori

International Journal of Computer Assisted Radiology and Surgery Vol. 12 page： S201-S203 2017.6

Multi-organ segmentation in abdominal CT using 3D fully convolutional networks

Holger R. Roth, Masahiro Oda, Yuichiro Hayashi, Hirohisa Oda, Kensaku Mori

International Journal of Computer Assisted Radiology and Surgery Vol. 12 page： S55-S57 2017.6

Endocytoscope image classification using deep convolutional neural networks

Masahiro Oda, Yutaka Hoshiyama, Masashi Misawa, Yuichi Mori, Kenichi Takeda, Sin-ei Kudo, Kensaku Mori

International Journal of Computer Assisted Radiology and Surgery Vol. 12 page： S147-S148 2017.6

False positive reduction of abdominal artery segmentation from CT volumes based on deep convolutional neural networks

Masahiro Oda, Tokunori Yamamoto, Yasushi Yoshino, Kensaku Mori

International Journal of Computer Assisted Radiology and Surgery Vol. 12 page： S27-S29 2017.6

Endocytoscope image classification using deep convolutional neural networks

Masahiro Oda, Yutaka Hoshiyama, Masashi Misawa, Yuichi Mori, Kenichi Takeda, Sin-ei Kudo, Kensaku Mori

International Journal of Computer Assisted Radiology and Surgery Vol. 12 page： S147-S148 2017.6

False positive reduction of abdominal artery segmentation from CT volumes based on deep convolutional neural networks

Masahiro Oda, Tokunori Yamamoto, Yasushi Yoshino, Kensaku Mori

International Journal of Computer Assisted Radiology and Surgery Vol. 12 page： S27-S29 2017.6

Automatic Anatomical Labeling of Arteries and Veins Using Conditional Random Fields

Takayuki Kitasaka, Mitsuru Kagajo, Yukitaka Nimura, Yuichiro Hayashi, Masahiro Oda, Kazunari Misawa, Kensaku Mori

8th International Conference on Information Processing in Computer-Assisted Interventions (IPCAI 2017) page： - 2017.6

Automatic Anatomical Labeling of Arteries and Veins Using Conditional Random Fields

Takayuki Kitasaka, Mitsuru Kagajo, Yukitaka Nimura, Yuichiro Hayashi, Masahiro Oda, Kazunari Misawa, Kensaku Mori

8th International Conference on Information Processing in Computer-Assisted Interventions (IPCAI 2017) page： - 2017.6

Automatic anatomical labeling of arteries and veins using conditional random fields Reviewed

Takayuki Kitasaka, Mitsuru Kagajo, Yukitaka Nimura, Yuichiro Hayashi, Masahiro Oda, Kazunari Misawa, Kensaku Mori

INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY Vol. 12 ( 6 ) page： 1041 - 1048 2017.6

Language：English Publishing type：Research paper (scientific journal) Publisher：SPRINGER HEIDELBERG

For safe and reliable laparoscopic surgery, it is important to determine individual differences of blood vessels such as the position, shape, and branching structures. Consequently, a computer-assisted laparoscopy that displays blood vessel structures with anatomical labels would be extremely beneficial. This paper details an automated anatomical labeling method for abdominal arteries and veins extracted from 3D CT volumes.
The proposed method represents a blood vessel tree as a probabilistic graphical model by conditional random fields (CRFs). An adaptive gradient algorithm is adopted for structure learning. The anatomical labeling of blood vessel branches is performed by maximum a posteriori estimation.
We applied the proposed method to 50 cases of arterial and portal phase abdominal X-ray CT volumes. The experimental results showed that the F-measure of the proposed method for abdominal arteries and veins was 94.4 and 86.9%, respectively.
We developed an automated anatomical labeling method to annotate each blood vessel branches of abdominal arteries and veins using CRF. The proposed method outperformed a state-of-the-art method.

DOI： 10.1007/s11548-017-1549-x

Web of Science

Scopus

PubMed

Accuracy of computer-aided diagnosis based on narrow-band imaging endocytoscopy for diagnosing colorectal lesions: comparison with experts Reviewed

Masashi Misawa, Shin-ei Kudo, Yuichi Mori, Kenichi Takeda, Yasuharu Maeda, Shinichi Kataoka, Hiroki Nakamura, Toyoki Kudo, Kunihiko Wakamura, Takemasa Hayashi, Atsushi Katagiri, Toshiyuki Baba, Fumio Ishida, Haruhiro Inoue, Yukitaka Nimura, Msahiro Oda, Kensaku Mori

INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY Vol. 12 ( 5 ) page： 757 - 766 2017.6

Language：English Publishing type：Research paper (scientific journal) Publisher：SPRINGER HEIDELBERG

Real-time characterization of colorectal lesions during colonoscopy is important for reducing medical costs, given that the need for a pathological diagnosis can be omitted if the accuracy of the diagnostic modality is sufficiently high. However, it is sometimes difficult for community-based gastroenterologists to achieve the required level of diagnostic accuracy. In this regard, we developed a computer-aided diagnosis (CAD) system based on endocytoscopy (EC) to evaluate cellular, glandular, and vessel structure atypia in vivo. The purpose of this study was to compare the diagnostic ability and efficacy of this CAD system with the performances of human expert and trainee endoscopists.
We developed a CAD system based on EC with narrow-band imaging that allowed microvascular evaluation without dye (ECV-CAD). The CAD algorithm was programmed based on texture analysis and provided a two-class diagnosis of neoplastic or non-neoplastic, with probabilities. We validated the diagnostic ability of the ECV-CAD system using 173 randomly selected EC images (49 non-neoplasms, 124 neoplasms). The images were evaluated by the CAD and by four expert endoscopists and three trainees. The diagnostic accuracies for distinguishing between neoplasms and non-neoplasms were calculated.
ECV-CAD had higher overall diagnostic accuracy than trainees (87.8 vs 63.4%; ), but similar to experts (87.8 vs 84.2%; ). With regard to high-confidence cases, the overall accuracy of ECV-CAD was also higher than trainees (93.5 vs 71.7%; ) and comparable to experts (93.5 vs 90.8%; ).
ECV-CAD showed better diagnostic accuracy than trainee endoscopists and was comparable to that of experts. ECV-CAD could thus be a powerful decision-making tool for less-experienced endoscopists.

DOI： 10.1007/s11548-017-1542-4

Web of Science

Scopus

PubMed

機械学習を用いた内視鏡画像自動診断

第56回日本生体医工学会大会プログラム・抄録集 page： 344 2017.5

3Dプリンタの医療応用

森　健策

医用画像情報学会雑誌 Vol. 34 ( 1 ) page： 1-6 2017.4

3Dプリンタの医療応用

森健策

医用画像情報学会雑誌 Vol. 34 ( 1 ) page： 1-6 2017.4

Development of an automatic producing cholangiography procedure reconstructed from portal phase multidetector-row computed tomography images: Preliminary experience Reviewed

Tomoaki Hirose, Tsuyoshi Igami, Kusuto Koga, Yuichiro Hayashi, Tomoki Ebata, Yukihiro, Yokoyama, Gen Sugawara, Takashi Mizuno, Junpei Yamaguchi, Kensaku Mori, Masato Nagino

Surgery Today Vol. 47 ( 3 ) page： 365-374 2017.3

Supervoxel classification forests for estimating pairwise image correspondences Reviewed

Fahdi Kanavati, Tong Tong, Kazunari Misawa, Michitaka Fujiwara, Kensaku Mori, Daniel Rueckert, Ben Glocker

Pattern Recognition Vol. 63 page： 561-569 2017.3

An improved method for pancreas segmentation using SLIC superpixels and interactive region merging

Liyuan Zhang, Huamin Yang, Weili Shi, Yu Miao, Fei He, Wei He, Yanfang Li, Fei Yan, Huimao Zhang, Kensaku Mori, Zhengang Jiang

Proceedings of SPIE Vol. 10134 page： 101343H-1-101343H-12 2017.2

Hessian-assisted supervoxel: structure-oriented voxel clustering and application to mediastinal lymph node detection from CT volumes

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

Proceedings of SPIE Vol. 10134 page： 101341D-1-101341D-12 2017.2

Computer-aided diagnosis of mammographic masses using geometric verification-based image retrieval

Qingliang Li, Weili SHI, Huamin Yang, Huimao Zhang, Tao CHEN, Kensaku Mori, Guoxin LI, Zhengang Jiang

Proceedings of SPIE Vol. 10134 page： 101342W-1-101342W-8 2017.2

Extracellular matrix directions estimation of the heart on microfocus X-ray CT volumes

Hirohisa Oda, Masahiro Oda, Takayuki Kitasaka, Toshiaki Akita, Kensaku Mori

Proceedings of SPIE Vol. 10137 page： 101370M-1-101370M-9 2017.2

Automatic segmentation of airway tree based on local intensity filter and machine learning technique in 3D chest CT volume Reviewed

Qier Meng, Takayuki Kitasaka, Yukitaka Nimura, Masahiro Oda, Junji Ueno, Kensaku Mori

INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY Vol. 12 ( 2 ) page： 245 - 261 2017.2

Language：English Publishing type：Research paper (scientific journal) Publisher：SPRINGER HEIDELBERG

Airway segmentation plays an important role in analyzing chest computed tomography (CT) volumes for computerized lung cancer detection, emphysema diagnosis and pre- and intra-operative bronchoscope navigation. However, obtaining a complete 3D airway tree structure from a CT volume is quite a challenging task. Several researchers have proposed automated airway segmentation algorithms basically based on region growing and machine learning techniques. However, these methods fail to detect the peripheral bronchial branches, which results in a large amount of leakage. This paper presents a novel approach for more accurate extraction of the complex airway tree.
This proposed segmentation method is composed of three steps. First, Hessian analysis is utilized to enhance the tube-like structure in CT volumes; then, an adaptive multiscale cavity enhancement filter is employed to detect the cavity-like structure with different radii. In the second step, support vector machine learning will be utilized to remove the false positive (FP) regions from the result obtained in the previous step. Finally, the graph-cut algorithm is used to refine the candidate voxels to form an integrated airway tree.
A test dataset including 50 standard-dose chest CT volumes was used for evaluating our proposed method. The average extraction rate was about 79.1 % with the significantly decreased FP rate.
A new method of airway segmentation based on local intensity structure and machine learning technique was developed. The method was shown to be feasible for airway segmentation in a computer-aided diagnosis system for a lung and bronchoscope guidance system.

DOI： 10.1007/s11548-016-1492-2

Web of Science

Scopus

PubMed

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Papers - MORI, Kensaku