Faculty Profiles - MORI, Kensaku

Papers - MORI, Kensaku

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Intestine segmentation with small computational cost for diagnosis assistance of ileus and intestinal obstruction Invited Reviewed

Hirohisa Oda, Yuichiro Hayashi, Takayuki Kitasaka, Aitaro Takimoto, Akinari Hinoki, Hiroo Uchida, Kojiro Suzuki, Masahiro Oda, Kensaku Mori

LNCS 12969 page： 3 - 12 2021.9

泌尿器科領域における画像処理

森健策

泌尿器科 Vol. 14 ( 2 ) page： 213 - 220 2021.8

Can artificial intelligence help to detect dysplasia in patients with ulcerative colitis? Invited International journal

Yasuharu Maeda, Shin-Ei Kudo, Noriyuki Ogata, Masashi Misawa, Yuichi Mori, Kensaku Mori, Kazuo Ohtsuka

Endoscopy Vol. 53 ( 07 ) page： E273 - E274 2021.7

Micro-CT-assisted cross-modality super-resolution of clinical CT: utilization of synthesized training dataset Reviewed

T. Zheng, H. Oda, S. Nakamura, M. Mori, H. Takabatake, H. Natori, M. Oda, K. Mori

International Journal of Computer Assisted Radiology and Surgery Vol. 16 ( sup.1 ) page： 12 - 14 2021.6

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

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

INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY Vol. 16 ( 6 ) page： 989 - 1001 2021.6

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

Purpose A three-dimensional (3D) structure extraction technique viewed from a two-dimensional image is essential for the development of a computer-aided diagnosis (CAD) system for colonoscopy. However, a straightforward application of existing depth-estimation methods to colonoscopic images is impossible or inappropriate due to several limitations of colonoscopes. In particular, the absence of ground-truth depth for colonoscopic images hinders the application of supervised machine learning methods. To circumvent these difficulties, we developed an unsupervised and accurate depth-estimation method. Method We propose a novel unsupervised depth-estimation method by introducing a Lambertian-reflection model as an auxiliary task to domain translation between real and virtual colonoscopic images. This auxiliary task contributes to accurate depth estimation by maintaining the Lambertian-reflection assumption. In our experiments, we qualitatively evaluate the proposed method by comparing it with state-of-the-art unsupervised methods. Furthermore, we present two quantitative evaluations of the proposed method using a measuring device, as well as a new 3D reconstruction technique and measured polyp sizes. Results Our proposed method achieved accurate depth estimation with an average estimation error of less than 1 mm for regions close to the colonoscope in both of two types of quantitative evaluations. Qualitative evaluation showed that the introduced auxiliary task reduces the effects of specular reflections and colon wall textures on depth estimation and our proposed method achieved smooth depth estimation without noise, thus validating the proposed method. Conclusions We developed an accurate depth-estimation method with a new type of unsupervised domain translation with the auxiliary task. This method is useful for analysis of colonoscopic images and for the development of a CAD system since it can extract accurate 3D information.

DOI： 10.1007/s11548-021-02398-x

Web of Science

Scopus

PubMed

AIによる内視鏡外科手術支援と開発基盤としての手術動画データベース構築 Invited Reviewed

竹下修由, 森健策, 伊藤正昭

消化器外科 Vol. 44 ( 7 ) page： 1159 - 1166 2021.6

Three-dimensional surgical plan printing for assisting liver surgery, Reviewed

Y. Hayashi, T. Igami, Y. Nakamura, K. Mori

International Journal of Computer Assisted Radiology and Surgery Vol. 16 ( sup.1 ) page： 104 - 106 2021.6

COVID-19 lung infection and normal region segmentation from CT volumes using FCN with local and global spatial feature encoder Reviewed

M. Oda, Y. Hayashi, Y. Otake, M. Hashimoto, T. Akashi, K. Mori

International Journal of Computer Assisted Radiology and Surgery Vol. 16 ( sup.1 ) page： 19 - 20 2021.6

Experimental evaluation of loss functions in YOLO-v3 training for the perforation detection and localization in colonoscopic videos Reviewed

K. Jiang, H. Itoh, M. Oda, T. Okumura, Y. Mori, M. Misawa, T. Hayashi, S. E. Kudo, K. Mori

International Journal of Computer Assisted Radiology and Surgery Vol. 16 ( sup.1 ) page： 74 - 75 2021.6

Blood vessel regions segmentation from laparoscopic videos using fully convolutional networks with multi field of view input Reviewed

K. Mori, S. Morimitsu, S. Yamamoto, T. Ozawa, T. Kitasaka, Y. Hayashi, M. Oda, M. Ito, N. Takeshita, K. Misawa

International Journal of Computer Assisted Radiology and Surgery Vol. 16 ( sup.1 ) page： 56 - 57 2021.6

`Intestine segmentation combining Watershed transformation and machine learning-based distance map estimation Reviewed

H. Oda, Y. Hayashi, T. Kitasaka, Y. Tamada, A. Takimoto, A. Hinoki, H. Uchida, K. Suzuki, H. Itoh, M. Oda, K. Mori

International Journal of Computer Assisted Radiology and Surgery Vol. 16 ( sup.1 ) page： 89 - 90 2021.6

Real-time deformation simulation of hollow organs based on XPBD with small time steps and air mesh for surgical simulation Reviewed

S. Li, Y. Hayashi, M. Oda, K. Misawa, K. Mori

International Journal of Computer Assisted Radiology and Surgery Vol. 16 ( sup.1 ) page： 21 - 25 2021.6

Spatial Information Considered Module based on Attention Mechanism for Self-Supervised Depth Estimation from Laparoscopic Image Pairs Reviewed

W. Li, Y. Hayashi, M. Oda, T. Kitasaka, K. Misawa, K. Mori

nternational Journal of Computer Assisted Radiology and Surgery Vol. 16 ( sup.1 ) page： 45 - 46 2021.6

機械学習によるCOVID-19症例CT画像の診断支援 Reviewed

森　健策

映像情報メディア学会誌 Vol. 75 ( 3 ) page： 326 - 329 2021.5

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

Masashi Misawa, Shin-Ei Kudo, Yuichi Mori, Kinichi Hotta, Kazuo Ohtsuka, Takahisa Matsuda, Shoichi Saito, Toyoki Kudo, Toshiyuki Baba, Fumio Ishida, Hayato Itoh, Masahiro Oda, Kensaku Mori

Gastrointestinal endoscopy Vol. 93 ( 4 ) page： 960 - 967 2021.4

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

BACKGROUND AND AIMS: Artificial intelligence (AI)-assisted polyp detection systems for colonoscopic use are currently attracting attention because they may reduce the possibility of missed adenomas. However, few systems have the necessary regulatory approval for use in clinical practice. We aimed to develop an AI-assisted polyp detection system and to validate its performance using a large colonoscopy video database designed to be publicly accessible. METHODS: To develop the deep learning-based AI system, 56,668 independent colonoscopy images were obtained from 5 centers for use as training images. To validate the trained AI system, consecutive colonoscopy videos taken at a university hospital between October 2018 and January 2019 were searched to construct a database containing polyps with unbiased variance. All images were annotated by endoscopists according to the presence or absence of polyps and the polyps' locations with bounding boxes. RESULTS: A total of 1405 videos acquired during the study period were identified for the validation database, 797 of which contained at least 1 polyp. Of these, 100 videos containing 100 independent polyps and 13 videos negative for polyps were randomly extracted, resulting in 152,560 frames (49,799 positive frames and 102,761 negative frames) for the database. The AI showed 90.5% sensitivity and 93.7% specificity for frame-based analysis. The per-polyp sensitivities for all, diminutive, protruded, and flat polyps were 98.0%, 98.3%, 98.5%, and 97.0%, respectively. CONCLUSIONS: Our trained AI system was validated with a new large publicly accessible colonoscopy database and could identify colorectal lesions with high sensitivity and specificity. (Clinical trial registration number: UMIN 000037064.).

DOI： 10.1016/j.gie.2020.07.060

Scopus

PubMed

Contrastive Learningを用いた肺野CT画像からCOVID-19の自動判定 Invited Reviewed

加藤聡太, 堀田一弘, 小田昌宏, 森健策, 大竹義人, 橋本正弘, 明石敏昭

電子情報通信学会技術研究報告(MI) Vol. 120 ( 432 ) page： 82 - 86 2021.3

カスケードCNNによる腹腔鏡動画からの出血領域自動抽出 Invited Reviewed

山本翔太, 林雄一郎, 盛満慎太郎, 北坂孝幸, 小田昌宏, 竹下修由, 伊藤雅昭, 森健策

電子情報通信学会技術研究報告(MI) Vol. 120 ( 431 ) page： 172 - 175 2021.3

Spectral-based Convolutional Graph Neural Networksを用いた腹部動脈領域の血管名自動命名に関する研究 Invited Reviewed

日比裕太, 林雄一郎, 北坂孝幸, 伊東隼人, 小田昌宏, 三澤一成, 森健策

電子情報通信学会技術研究報告(MI) Vol. 120 ( 431 ) page： 176 - 181 2021.3

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

Shin-Ei Kudo, Katsuro Ichimasa, Benjamin Villard, Yuichi Mori, Masashi Misawa, Shoichi Saito, Kinichi Hotta, Yutaka Saito, Takahisa Matsuda, Kazutaka Yamada, Toshifumi Mitani, Kazuo Ohtsuka, Akiko Chino, Daisuke Ide, Kenichiro Imai, Yoshihiro Kishida, Keiko Nakamura, Yasumitsu Saiki, Masafumi Tanaka, Shu Hoteya, Satoshi Yamashita, Yusuke Kinugasa, Masayoshi Fukuda, Toyoki Kudo, Hideyuki Miyachi, Fumio Ishida, Hayato Itoh, Masahiro Oda, Kensaku Mori

Gastroenterology Vol. 160 ( 4 ) page： 1075 - 1084.e2 2021.3

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

BACKGROUND & AIMS: In accordance with guidelines, most patients with T1 colorectal cancers (CRC) undergo surgical resection with lymph node dissection, despite the low incidence (∼10%) of metastasis to lymph nodes. To reduce unnecessary surgical resections, we used artificial intelligence to build a model to identify T1 colorectal tumors at risk for metastasis to lymph node and validated the model in a separate set of patients. METHODS: We collected data from 3134 patients with T1 CRC treated at 6 hospitals in Japan from April 1997 through September 2017 (training cohort). We developed a machine-learning artificial neural network (ANN) using data on patients' age and sex, as well as tumor size, location, morphology, lymphatic and vascular invasion, and histologic grade. We then conducted the external validation on the ANN model using independent 939 patients at another hospital during the same period (validation cohort). We calculated areas under the receiver operator characteristics curves (AUCs) for the ability of the model and US guidelines to identify patients with lymph node metastases. RESULTS: Lymph node metastases were found in 319 (10.2%) of 3134 patients in the training cohort and 79 (8.4%) of /939 patients in the validation cohort. In the validation cohort, the ANN model identified patients with lymph node metastases with an AUC of 0.83, whereas the guidelines identified patients with lymph node metastases with an AUC of 0.73 (P < .001). When the analysis was limited to patients with initial endoscopic resection (n = 517), the ANN model identified patients with lymph node metastases with an AUC of 0.84 and the guidelines identified these patients with an AUC of 0.77 (P = .005). CONCLUSIONS: The ANN model outperformed guidelines in identifying patients with T1 CRCs who had lymph node metastases. This model might be used to determine which patients require additional surgery after endoscopic resection of T1 CRCs. UMIN Clinical Trials Registry no: UMIN000038609.

DOI： 10.1053/j.gastro.2020.09.027

Scopus

PubMed

Label cleaning and propagation for improved segmentation performance using fully convolutional networks. Invited Reviewed International journal

Takaaki Sugino, Yutaro Suzuki, Taichi Kin, Nobuhito Saito, Shinya Onogi, Toshihiro Kawase, Kensaku Mori, Yoshikazu Nakajima

International journal of computer assisted radiology and surgery Vol. 16 ( 3 ) page： 349 - 361 2021.3

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

PURPOSE: In recent years, fully convolutional networks (FCNs) have been applied to various medical image segmentation tasks. However, it is difficult to generate a large amount of high-quality annotation data to train FCNs for medical image segmentation. Thus, it is desired to achieve high segmentation performances even from incomplete training data. We aim to evaluate performance of FCNs to clean noises and interpolate labels from noisy and sparsely given label images. METHODS: To evaluate the label cleaning and propagation performance of FCNs, we used 2D and 3D FCNs to perform volumetric brain segmentation from magnetic resonance image volumes, based on network training on incomplete training datasets from noisy and sparse annotation. RESULTS: The experimental results using pseudo-incomplete training data showed that both 2D and 3D FCNs could provide improved segmentation results from the incomplete training data, especially by using three orthogonal annotation images for network training. CONCLUSION: This paper presented a validation for label cleaning and propagation based on FCNs. FCNs might have the potential to achieve improved segmentation performances even from sparse annotation data including possible noises by manual annotation, which can be an important clue to more efficient annotation.

DOI： 10.1007/s11548-021-02312-5

Web of Science

Scopus

PubMed

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Papers - MORI, Kensaku