Abstract
Coronary artery segmentation in X-ray angiography is clinically critical for percutaneous coronary intervention (PCI), as it offers essential morphological guidance for stent deployment, stenosis assessment, and hemodynamic optimization. Nevertheless, inherent angiographic limitations, including complex vasculature, low contrast, and fuzzy boundaries, persist as significant challenges. Current methodologies exhibit notable shortcomings, including fragmented output continuity, noise susceptibility, and computational inefficiency. This study proposes VM-CAGSeg, a novel U-shaped architecture integrating vessel structure-aware state space modeling, to address these limitations. The framework introduces three key innovations: (1) A Vessel Structure-Aware State Space (VSASS) block that synergizes geometric priors from a Multiscale Vessel Structure-Aware (MVSA) module with long-range contextual modeling via Kolmogorov-Arnold State Space (KASS) blocks. The MVSA module enhances tubular feature representation through Hessian eigenvalue-derived vesselness measures. (2) A Cross-Stage Feature Interaction Fusion (CSFIF) module that replaces conventional skip connections with cross-stage feature fusion strategies to enhance the variability of learned features, preserving long-range dependencies and fine-grained details. (3) A unified architecture that integrates the Vessel Structure-Aware State Space (VSASS) block and the Cross-Stage Feature Interaction Fusion (CSFIF) module to achieve comprehensive vessel segmentation by synergizing multiscale geometric awareness, long-range dependency modeling, and cross-stage feature refinement. Experiments demonstrate that VM-CAGSeg achieves state-of-the-art performance, surpassing CNN-based (e.g., UNet++), transformer-based (e.g., MISSFormer), and state space model (SSM)-based (e.g., H_vmunet) methods, with a Dice similarity coefficient (DSC) of 88.15%, mIoU of 79.19%, and a 95% Hausdorff distance (HD95) of 13.68 mm. The framework significantly improved boundary delineation, reducing HD95 by 49.8% compared to UNet++ (27.15 mm) and by 16.6% compared to TransUNet (15.85 mm). While its sensitivity (90.05%) was marginally lower than that of TransUNet (90.33%), the model's balanced performance in segmentation accuracy and edge precision confirmed its robustness. These findings validate the effectiveness of integrating multiscale vessel-aware modeling, long-range dependency learning, and cross-stage feature fusion, making VM-CAGSeg a reliable solution for clinical vascular segmentation tasks that require fine-grained detail preservation. The proposed method is available as an open-source project at https://github.com/GIT-HYQ/VM-CAGSeg.