Key-Point Matching Guided Coronary Artery Extraction from CT Coronary Angiography Sequence

Extracting coronary arteries is important in clinical applications of cardiac Computed Tomography Angiography (CTA). However, obtaining an accurate segmentation of coronary artery is challenging because of complex anatomy, variation in image quality, as well as motion induced by heart beating and respiration. This paper presented a semi-automatic framework dedicated to coronary artery segmentation in 3D+t Computed Tomography Angiography (CTA) sequence. The proposed segmentation scheme consists of two parts: vessel centerline extraction and vessel shape recovering. For the starting phase, spherical flux based minimal path method was employed to extract the main centerlines of coronary artery tree. To implement similar centerline extraction scheme automatically in subsequent phases, a novel method key-points detecting method was proposed and an endpoints searching procedure was taken. In this process, the key-points on distal vessels are easily got mismatched. A centerline tracking algorithm constrained by patient-specific vessel length was used to complete the extraction. The the overlap (OV) results of proposed framework are 93.56% (LAD), 90.96% (LCX) and 93.76% (RCA). We also achieved accuracy (AC) r:esult: 0.6508mm (LAD) 0.6141mm (LCX) and 0.6297mm (RCA) which are less than the data voxel size (0.68mm).