Automatic 3-D tubular centerline tracking of coronary arteries in coronary computed tomographic angiography

Abstract

Purpose: Coronary Computed Tomography Angiography (CCTA) is a promising alternative for high accuracy detection of a wide range of coronary artery diseases. To achieve the anatomical and pathological features of intramuscular coronary arteries with minimal user interaction, we need an automated coronary artery centerline extraction algorithm. Method: This article presents a fully automatic coronary artery centerline tracking algorithm. First, a complex continuous wavelet transform with the Gaussian kernels is used to reduce noise effect. Then, a multiple hypothesis tracking approach is applied to segment 3-D vessel structures. Finally, the tracking procedure is completed by applying a newly presented branch searching approach based on region growing algorithm and a mathematical morphology operation. Results: The performance of the presented method is measured on the publicly available Rotterdam Coronary Artery Algorithm Evaluation Framework. The extraction ability of the algorithm computed by overlap measures averaged over 32 data-sets including overall overlap, overlap until the first error and overlap with the clinically relevant part of the vessel were OV = 85.2%, OF = 75.7% and OT = 98.5%, respectively. Also the average accuracy measurement was 0.26 mm which shows high extraction accuracy with respect to mean voxel size 0.32 × 0.32 × 0.4 mm3. Conclusion: The average coronary artery extraction time was about 8 min per data-set. The experiment results show that our newly developed algorithm achieved high efficiency in coronary artery centerlines tracking for CCTA images.