Mask R-CNN based coronary artery segmentation in coronary computed tomography angiography

Yabo Fu; Bangjun Guo; Yang Lei; Tonghe Wang; Tian Liu; Walter Curran; Longjiang Zhang; Xiaofeng Yang

doi:10.1117/12.2550588

16 March 2020 Mask R-CNN based coronary artery segmentation in coronary computed tomography angiography

Yabo Fu, Bangjun Guo, Yang Lei, Tonghe Wang, Tian Liu, Walter Curran, Longjiang Zhang, Xiaofeng Yang

Proceedings Volume 11314, Medical Imaging 2020: Computer-Aided Diagnosis; 113144F (2020) https://doi.org/10.1117/12.2550588
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

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.

Citation Download Citation

Yabo Fu, Bangjun Guo, Yang Lei, Tonghe Wang, Tian Liu, Walter Curran, Longjiang Zhang, and Xiaofeng Yang "Mask R-CNN based coronary artery segmentation in coronary computed tomography angiography", Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 113144F (16 March 2020); https://doi.org/10.1117/12.2550588

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