Abstract

X-ray coronary angiography remains the gold standard for diagnosing coronary heart disease. However, accurate segmentation is challenged by the subtlety of fine vascular features, topological discontinuities, and blurred boundaries in these images. Existing methods often struggle to capture both the long-range global topology and the fine local details required for robust segmentation. To address these issues, we propose BRNet, a framework that integrates a dual-backbone collaborative mechanism with multi-scale fusion and dynamic detail reconstruction. Our approach first employs a Vascular Local Detail Attention Module that combines ResNet18’s local perception with BiFormer’s global modeling, using SimAM parameter-free attention to suppress background noise. We then design a Vascular Structure-Coherent Progressive Fusion Module, which uses a top-down pyramid semantic flow to ensure topological coherence across different vascular scales. Finally, a Vascular Enhancement Dynamic Upsampling Module replaces traditional interpolation with content-aware CARAFE operators to achieve high-resolution reconstruction of blurred edges. Experiments on the public ARCADE dataset and the constructed heterogeneous benchmark ZZ-CAHDS show that BRNet achieves superior performance, attaining IoU scores of 0.6305 and 0.6774, and clDice coefficients of 0.7655 and 0.8355, respectively, and achieving a good balance between segmentation accuracy and computational efficiency. These results highlight BRNet’s effectiveness on retrospective segmentation benchmarks, demonstrating its capability for computer-assisted coronary artery segmentation.

IPC Classification

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