Preoperative Decision‐Oriented Decoupled Multi‐Scale Feature Pyramid and Global Context Aggregation Network for Coronary Artery Segmentation in X‐Ray Angiography

BACKGROUND: Accurate preoperative coronary artery delineation in X-ray angiography is critical for stenosis quantification, cardiovascular diagnosis, and treatment planning, yet class imbalance, weak bifurcation contrast, and overlapping artefacts often cause broken or distorted vessel segmentation. METHODS: We propose a decoupled hybrid architecture that separates multi-scale detail recovery from global context aggregation, combining an FPN neck for distal-vessel edge preservation with a UPerHead head for multi-level contextual prior modelling. RESULTS: On the ARCADE benchmark, the model achieves Dice, IoU, and Centreline Dice scores of 0.7633, 0.6290, and 0.7827, outperforming competitive baseline methods in terms of Dice, IoU, and Centreline Dice, with consistent but moderate improvements over the strongest competing model. Grad-CAM++ and feature-distribution analyses suggest improved contextual attention and enhanced interpretability of learnt representations. CONCLUSIONS: The framework enhances robust preoperative vessel segmentation, supporting assessment of distal continuity, bifurcation morphology, and lesion-adjacent boundaries for clinical decision-making.