Automated coronary artery segmentation in Coronary Computed Tomography Angiography (CCTA) using deep learning neural networks

Abstract

Automatic segmentation of the coronary artery in coronary computed tomographic angiography (CCTA) is important for clinicians in evaluating patients with coronary artery disease (CAD). Tradition visual interpretation of coronary artery stenosis is observer-dependent and time-consuming. In this work, we proposed to use a 3D attention fully convolution network (FCN) method to automatically segment the coronary artery for CCTA. FCN was used to perform end-to-end mapping from CCTA image to the binary segmentation of coronary artery. Deep attention strategy was integrated into the FCN model to highlight the informative semantic features extracted from CCTA image and thus to enhance the accuracy of segmentation. The proposed method was tested on 30 patients’ CCTA data. Dice similarity coefficient (DSC), precision and recall indices between manually delineated coronary artery contour and segmented contour were used to quantify the segmentation accuracy of the proposed method. The DSC, precision, and recall were 83%±4%, 84%±4% and 87%±3%, which demonstrated the segmentation accuracy of the proposed method.