Coronary Computed Tomography angiography (CCTA) represents the non-invasive method of choice for the assessment of coronary artery disease. Accurate multi-planar reconstructions (MPR) using centerlines are vital for detecting stenosis and extracting radiomic features. However, manual delineation centerline delineation is prohibitively time-consuming in clinical practice. Current automated tools present significant limitations, including extensive user interactions requirements, inaccurate centerline tracing (particularly at vessel bifurcations), and the inability to process multiple cases simultaneously.

This study aimed to develop a fully automated deep-learning (DL) method to extract topology-preserving coronary lumen centerlines from CCTA images.