Automated segmentation and tracking of coronary arteries in cardiac CT scans: comparison of performance with a clinically used commercial software

摘要

Coronary CT angiography (cCTA) has been reported to be an effective means for diagnosis of coronary artery disease. We are investigating the feasibility of developing a computer-aided detection (CADe) system to assist radiologists in detection of non-calcified plaques in coronary arteries in ECG-gated cCTA scans. In this study, we developed a prototype vessel segmentation and tracking method to extract the coronary arterial trees which will define the search space for plaque detection. Vascular structures are first enhanced by 3D multi-scale filtering and analysis of the eigenvalues of Hessian matrices using a vessel enhancement response function specifically designed for coronary arteries. The enhanced vascular structures are then segmented by an EM estimation method. The segmented coronary arteries are tracked using a 3D dynamic balloon tracking (DBT) method. For this preliminary study, two starting seed points were manually identified at the origins of the left and right coronary artery (LCA and RCA). The DBT method automatically moves a sphere along the vessel whose diameter is adjusted dynamically based on the local vessel size, tracks the vessels, and identifies its branches to generate the left and right coronary arterial trees. The algorithm was applied to 20 cCTA scans that contained various degrees of coronary artery diseases. To evaluate the performance of vessel segmentation and tracking, the rendered volume of coronary arteries tracked by our algorithm was displayed on a PC, placed next to a GE Advantage workstation on which the coronary arterial trees tracked by the GE software and the original cCTA scan were displayed. Two experienced thoracic radiologists visually examined the coronary arteries on the cCTA scan and the segmented vessels to count untracked false-negative (FN) segments and false positives (FPs). The comparison was made by radiologists' visual judgment because the digital files for the segmented vessels were not accessible on the commercial system. A total of 19 and 38 artery segments were identified to be FNs, and 23 FPs and 20 FPs were found in the coronary trees tracked by our algorithm and the GE software, respectively. The preliminary results demonstrated the feasibility of our approach.