ArticleThe international journal of medical robotics + computer assisted surgery : MRCAS2026
Preoperative Decision-Oriented Decoupled Multi-Scale Feature Pyramid and Global Context Aggregation Network for Coronary Artery Segmentation in X-Ray Angiography.
Article in The international journal of medical robotics + computer assisted surgery : MRCAS, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundAccurate 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.
methodsWe 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.
resultsOn 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.
conclusionsThe framework enhances robust preoperative vessel segmentation, supporting assessment of distal continuity, bifurcation morphology, and lesion-adjacent boundaries for clinical decision-making.
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