A Novel End-to-End Deep Learning Solution for Automatic Coronary Artery Segmentation from CCTA: Algorithm Development and Validation Study (Preprint)

BACKGROUND Coronary computed tomographic angiography (CCTA) plays a vital role in the diagnosis of cardiovascular diseases, among which automatic Coronary Artery Segmentation (CAS) serves as one of the most challenging tasks. To computationally assist the task, this paper proposes a novel DL solution. OBJECTIVE This study introduces an end-to-end novel deep learning-based (DL) solution for automatic CAS. METHODS Inspired by the Di-Vnet network, a fully automatic multistage DL solution is proposed. The new solution aims to preserve the integrity of blood vessels in terms of both their shape details and continuity. The solution is developed using 338 CCTA cases, among which 133 cases (33865 axial images) have their groundtruth cardiac masks pre-annotated and 205 cases (53365 axial images) have their groundtruth coronary artery (CA) masks pre-annotated. DSC and 95% HD scores are used to measure the solution’s accuracy in CAS. RESULTS The proposed solution attains (90.29±1.38) % in its DSC and (2.11±0.24) mm in its 95% HD respectively, which consumes 0.112 seconds per image and 30 seconds per case on average. CONCLUSIONS The proposed solution attains (90.29±1.38) % in its DSC and (2.11±0.24) mm in its 95% HD respectively, which consumes 0.112 seconds per image and 30 seconds per case on average.