Automatic coronary artery labeling is essential for accurate vascular identification and the diagnosis of coronary disease. The task requires delineating the full vasculature and classifying each segment; however, preserving global topology and local demarcation line precision is difficult due to complex anatomy and blurry contours. We propose a coarse-to-fine ensemble framework with two modules: a Coarse-to-fine Topology Extraction (CTE) network using topology priors for global continuity, and a Progressive Vessel Labeling (PVL) module with multibranch fusion for segmentation and classification. Experiments on the ARCADE dataset achieve a mean F1-score of 0.6028, outperforming state-of-the-art methods and enhancing topological integrity and labeling accuracy. Code: https://github.com/IPMINWU/PGSMODEL.

High-fidelity, patient-specific cardiac mesh reconstruction underpins diagnosis, surgical planning, and hemodynamic simulation. Accurate and topologically coherent reconstruction remains challenging due to large inter-individual anatomical variability and complex cardiac morphology. We propose a template-free framework, TFSG, that integrates an Adaptive Structural Point Generation (ASG) module and a Structural Consistency Constraint (SCC). ASG extracts patientspecific anatomical landmarks from a point-cloud representation to guide deformation, while SCC enforces multi-level consistency (point distance, normal alignment and structural-point relations) to suppress topological and structural errors. Experiments on the CARE2025 WHS dataset show TFSG improves segmentation and mesh reconstruction quality compared to prior methods. Code: https://github.com/IPMI-NWU/TFSG.