Geometric Morphology Based Irrelevant Vessels Removal For Accurate Coronary Artery Segmentation.

Accurate semantic segmentation of coronary artery for CT images is critical in both coronary-related disease diagnosis (e.g., stenosis detection and plaque grading) and further intervention treatments. Considering the irrelevant tubular structures are usually difficult to be distinguished from the coronary arteries, e.g., veins, existing methods inevitably lead to false positives. In this paper, we incorporate the voxel and point cloud based segmentation methods into a coarse-to-fine framework for accurate coronary artery segmentation from Coronary Computed Tomography Angiography (CCTA) images. Specifically, after the coarse segmentation from any appealing voxel-based framework, initial segmentation maps are converted into point clouds and fed into a Refinement Module to filter out the irrelevant tubular vessels. In practice, the Refinement Module adopts the local feature aggregation on point clouds for contextual learning, capturing the geometric morphology of the coronary arteries. Furthermore, the first annotated CCTA dataset for coronary artery segmentation, named CORONARY-481, is released in this paper. Extensive experiments indicate that the proposed approach achieves state-of-the-art performance in coronary artery segmentation, improving the dice metric by 10% and preserving its fine structure as well.