研究者詳細 - 森　健策

論文 - 森　健策

分割表示 1,772 件中 221 - 240 件目／全件表示 >>

Micro-CT-assisted cross-modality super-resolution of clinical CT: utilization of synthesized training dataset 査読有り

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

International Journal of Computer Assisted Radiology and Surgery 16 巻 ( sup.1 ) 頁： 12 - 14 2021年6月

Unsupervised colonoscopic depth estimation by domain translations with a Lambertian-reflection keeping auxiliary task 招待有り査読有り

Itoh, H; Oda, M; Mori, Y; Misawa, M; Kudo, SE; Imai, K; Ito, S; Hotta, K; Takabatake, H; Mori, M; Natori, H; Mori, K

INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY 16 巻 ( 6 ) 頁： 989 - 1001 2021年6月

記述言語：英語掲載種別：研究論文（学術雑誌）出版者・発行元：International Journal of Computer Assisted Radiology and Surgery

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招待有り査読有り

竹下修由, 森健策, 伊藤正昭

消化器外科 44 巻 ( 7 ) 頁： 1159 - 1166 2021年6月

Three-dimensional surgical plan printing for assisting liver surgery, 査読有り

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

International Journal of Computer Assisted Radiology and Surgery 16 巻 ( sup.1 ) 頁： 104 - 106 2021年6月

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

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

International Journal of Computer Assisted Radiology and Surgery 16 巻 ( sup.1 ) 頁： 19 - 20 2021年6月

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

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 16 巻 ( sup.1 ) 頁： 74 - 75 2021年6月

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

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 16 巻 ( sup.1 ) 頁： 56 - 57 2021年6月

`Intestine segmentation combining Watershed transformation and machine learning-based distance map estimation 査読有り

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 16 巻 ( sup.1 ) 頁： 89 - 90 2021年6月

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

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

International Journal of Computer Assisted Radiology and Surgery 16 巻 ( sup.1 ) 頁： 21 - 25 2021年6月

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

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

nternational Journal of Computer Assisted Radiology and Surgery 16 巻 ( sup.1 ) 頁： 45 - 46 2021年6月

機械学習によるCOVID-19症例CT画像の診断支援査読有り

森　健策

映像情報メディア学会誌 75 巻 ( 3 ) 頁： 326 - 329 2021年5月

Development of a computer-aided detection system for colonoscopy and a publicly accessible large colonoscopy video database (with video) 招待有り査読有り国際誌

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

Gastrointestinal Endoscopy 93 巻 ( 4 ) 頁： 960 - 967 2021年4月

記述言語：英語掲載種別：研究論文（学術雑誌）出版者・発行元：Gastrointestinal Endoscopy

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の自動判定招待有り査読有り

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) 120 巻 ( 432 ) 頁： 82 - 86 2021年3月

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招待有り査読有り

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) 120 巻 ( 431 ) 頁： 172 - 175 2021年3月

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招待有り査読有り

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) 120 巻 ( 431 ) 頁： 176 - 181 2021年3月

Artificial Intelligence System to Determine Risk of T1 Colorectal Cancer Metastasis to Lymph Node. 招待有り査読有り国際誌

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 160 巻 ( 4 ) 頁： 1075 - 1084.e2 2021年3月

記述言語：英語掲載種別：研究論文（学術雑誌）出版者・発行元：Gastroenterology

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 招待有り査読有り国際誌

Sugino, T; Suzuki, Y; Kin, T; Saito, N; Onogi, S; Kawase, T; Mori, K; Nakajima, Y

INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY 16 巻 ( 3 ) 頁： 349 - 361 2021年3月

記述言語：英語掲載種別：研究論文（学術雑誌）出版者・発行元：International Journal of Computer Assisted Radiology and Surgery

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

Unsupervised segmentation of COVID-19 infected lung clinical CT volumes using image inpainting and representation learning 査読有り

Tong Zheng, Masahiro Oda, Chenglong Wang, Takayasu Moriya, Yuichiro Hayashi, Yoshito Otake, Masahiro Hashimoto, Toshiaki Akashi, Masaki Mori, Hirotsugu Takabatake, Hiroshi Natori, Kensaku Mori

Proc. SPIE 11596, Medical Imaging, 2021: Image Processing 頁： 115963F-1 - 6 2021年2月

Extremely imbalanced subarachnoid hemorrhage detection based on enseNet-LSTM network with class-balanced loss and transfer learning 査読有り

Zhongyang Lu, Masahiro Oda, Yuichiro Hayashi, Tao Hu, Hayato Itoh, Takeyuki Watadani, Osamu Abe, Masahiro Hashimoto, Masahiro Jinzaki, Kensaku Mori

Proceedings Volume 11597, Medical Imaging 2021: Computer-Aided Diagnosis 頁： 115971Z 2021年2月

Single-shot three-dimensional reconstruction for colonoscopic image analysis 査読有り

Hayato Itoh, Masahiro Oda, Yuichi Mori, Masashi Misawa, Shin-ei Kudo, Kinnichi Hotta, Hirotsugu Takabatake, Masaki Mori, Hiroshi Natori, Kensaku Mori

Proc. SPIE 11598, Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling 頁： 115980E-1 - 6 2021年2月

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