SFD-Mamba2Net: Structure-Guided Frequency-Enhanced Dual-Stream Mamba2 Network for Coronary Artery Segmentation

Coronary artery disease (CAD), primarily caused by arterial stenosis or occlusion, remains a leading cause of mortality worldwide. Invasive coronary angiography (ICA) is the clinical gold standard for CAD diagnosis and treatment planning, yet its effectiveness critically depends on accurate coronary artery segmentation and stenosis assessment. However, ICA images are often affected by low contrast, severe noise, and complex multi-scale vascular morphology, substantially challenges existing segmentation methods. This study aims to improve the robustness and reliability of ICA-based coronary artery segmentation and stenosis detection by learning structure-consistent representations that jointly capture geometric priors, cross-dataset variability, long-range spatial dependencies, and frequency-aware vascular features. We propose SFD-Mamba2Net , an end-to-end framework for coronary artery segmentation and stenosis detection. A Curvature-Aware Structural Enhancement (CASE) module is introduced in the encoder to highlight elongated vascular structures while suppressing background interference. To alleviate performance degradation induced by inter-dataset discrepancies, a Supervised Domain Adaptation (SDA) strategy is incorporated at the bottleneck to encourage domain-invariant feature learning. Long-range spatial dependencies and complex vessel topology are modeled via an Axial-Alternating Dual-Stream Mamba2 (AA-DS Mamba2) module. In the decoder, a Progressive High-Frequency Perception (PHFP) module integrates multi-level wavelet decomposition with deep convolution to refine high-frequency vascular details while preserving global structural. Experiments on three large-scale ICA datasets show that SFD-Mamba2Net achieves superior segmentation accuracy and strong cross-domain generalization, obtaining Dice scores of 89.31%, 77.01%, and 74.14% on JSPH-ICA, DCA1, and ARCADE, respectively. It also achieves high true positive and positive predictive values in stenosis detection, demonstrating robustness and clinical potential.