A Synergistic Multi-Scale Attention and Composite Feature Extraction Network for Coronary Artery Segmentation

Accurate coronary artery segmentation from two-dimensional Digital Subtraction Angiography (DSA) images is paramount for robot-assisted percutaneous coronary intervention (PCI). Still, it is severely challenged by complex background artifacts, the intricate morphology of fine vascular branches, and frequent discontinuities in segmentation. These inherent difficulties often render conventional segmentation approaches inadequate for the stringent precision demands of surgical navigation. To address these limitations, we propose a novel deep learning framework incorporating a Composite Feature Extraction Module (CFEM) and a Multi-scale Composite Attention Module (MCAM) within a U-shaped architecture. The CFEM is meticulously designed to capture tubular vascular characteristics and adapt to diverse vessel scales. In contrast, the MCAM, strategically embedded in skip connections, synergistically integrates multi-scale convolutions, spatial attention, and lightweight channel attention to enhance the perception of fine branches and model long-range dependencies, thereby improving topological connectivity. Additionally, a combined Dice-Focal loss function is employed to optimize segmentation boundary accuracy and mitigate class imbalance jointly. Extensive experiments on the public ARCADE dataset demonstrate that our method significantly outperforms state-of-the-art approaches, achieving a Dice coefficient of 76.74%, a clDice of 50.30%, and an HD95 of 57.84 pixels. These quantitative improvements in segmentation accuracy, vascular connectivity, and edge precision underscore its substantial clinical potential for providing robust vascular structure information in robot-assisted interventional surgery.