Global-to-Focal: Topology-Guided Progressive Refinement Network for Accurate Coronary Artery Segmentation

Automatic coronary artery segmentation is crucial for computer-aided diagnosis and treatment planning of coronary artery disease (CAD). It helps clinicians identify potential stenotic lesions and formulate treatment plans, thereby improving the efficiency and effectiveness of diagnosis and treatment. However, the complex tree-like tubular structure of the coronary artery makes it challenging to accurately identify small branches, leading to incomplete topology. This paper proposes a topology-guided progressive refinement network (TPRNet) that progresses from global to focal perspective, leveraging the anatomical topology of the coronary artery to accurately identify small branches and reconstruct vascular structure. Specifically, the globalnet branch performs global segmentation to capture the spatial location information of the coronary artery in the image; the localnet branch segments local vessel regions based on location information and extracts vascular topology; the focalnet branch performs fine-grained segmentation along the centerline to capture vascular details; and finally, the refinement branch reconstructs and optimizes the topology. Experiments show that TPRNet outperforms existing methods on the public coronary artery segmentation dataset ARCADE. The code is available at https://github.com/IPMINWU/TPRNet.