Uncertainty-Guided Spectral Adaptation for Coronary Artery Segmentation

Coronary artery segmentation in invasive coronary angiography (ICA) is hindered by changes in imaging conditions and ambiguous vessel boundaries. These challenges are particularly pronounced for thin, low-contrast branches, whose structural details are easily lost and whose uncertain pixels can compromise cross-domain alignment. We propose FUP-Net, a Frequency-aware Uncertainty-guided Prototype Network that couples spectral representation learning with uncertainty-guided adaptation. Its Frequency-aware Heterogeneous Mamba2 (FH-Mamba2) module captures complementary frequency information by combining long-range vascular modeling with convolutional processing of local details. Building on these features, an Uncertainty-guided Prototype Adaptation (UPA) mechanism reduces the influence of ambiguous pixels when updating semantic prototypes, enabling more reliable alignment across imaging domains. Experiments on multiple ICA benchmarks assess segmentation accuracy, vascular topology, and cross-domain generalization. FUP-Net improves Dice by 2.28, 7.40, and 3.65 percentage points over the strongest baselines on JSPH-ICA, DCA1, and ARCADE, respectively. Ablation studies further validate the contributions of frequency-aware modeling and uncertainty-guided prototype adaptation.