Robust and Accurate Coronary Artery Centerline Extraction in CTA by Combining Model-Driven and Data-Driven Approaches

Various methods have been proposed to extract coronary artery centerlines from computed tomography angiography (CTA) data. Almost all previous approaches are data-driven, which try to trace a centerline from an automatically detected or manually specified coronary ostium. No or little high level prior information is used; therefore, the centerline tracing procedure may terminate early at a severe occlusion or an anatomically inconsistent centerline course may be generated. Though the connectivity of coronary arteries exhibits large variations, the position of major coronary arteries relative to the heart chambers is quite stable. In this work, we propose to exploit the automatically segmented chambers to 1) predict the initial position of the major coronary centerlines and 2) define a vessel-specific region-of-interest (ROI) to constrain the following centerline refinement. The proposed prior constraints have been integrated into a model-driven algorithm for the extraction of three major coronary centerlines, namely the left anterior descending artery (LAD), left circumflex artery (LCX), and right coronary artery (RCA). After extracting the major coronary arteries, the side branches are traced using a data-driven approach to handle large anatomical variations in side branches. Experiments on the public Rotterdam coronary CTA database demonstrate the robustness and accuracy of the proposed method. We achieve the best average ranking on overlap metrics among automatic methods and our accuracy metric outperforms all other 22 methods (including both automatic and semi-automatic methods).