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Proceedings Paper

Mask R-CNN based coronary artery segmentation in coronary computed tomography angiography
Author(s): Yabo Fu; Bangjun Guo; Yang Lei; Tonghe Wang; Tian Liu; Walter Curran; Longjiang Zhang; Xiaofeng Yang
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Paper Abstract

Automated segmentation of the coronary artery in coronary computed tomographic angiography (CCTA) is important for clinicians in evaluating patients with coronary artery disease. Tradition visual interpretation of coronary artery stenosis exist inter-observer variability and time-consuming. The purpose of this work is to develop a deep learningbased framework for coronary artery segmentation on CCTA. We propose to use Mask R-CNN for the coronary artery segmentation. To avoid the interferences from pulmonary vessels, we propose to mask out the lung region prior to Mask R-CNN training. The network was trained using 20 patients’ CCTA datasets and tested using another 5 patients’ CCTA datasets. The mean of the Dice similarity coefficient (DSC) were 0.90±0.01 respectively, which demonstrated the segmentation accuracy of the proposed method.

Paper Details

Date Published: 16 March 2020
PDF: 6 pages
Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 113144F (16 March 2020); doi: 10.1117/12.2550588
Show Author Affiliations
Yabo Fu, Emory Univ. (United States)
Bangjun Guo, Emory Univ. (United States)
Southern Medical Univ. (China)
Nanjing Univ. (China)
Yang Lei, Emory Univ. (United States)
Tonghe Wang, Emory Univ. (United States)
Tian Liu, Emory Univ. (United States)
Walter Curran, Emory Univ. (United States)
Longjiang Zhang, Southern Medical Univ. (China)
Nanjing Univ. (China)
Xiaofeng Yang, Emory Univ. (United States)


Published in SPIE Proceedings Vol. 11314:
Medical Imaging 2020: Computer-Aided Diagnosis
Horst K. Hahn; Maciej A. Mazurowski, Editor(s)

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