TAF-Net: Temporal-Adaptive Fusion Framework for Semisupervised Segmentation of Intracranial Arteries in DSA Sequences

Accurate segmentation of intracranial arteries in digital subtraction angiography (DSA) sequences is critical for cerebrovascular diagnosis but remains challenging due to limited annotations and complex vascular structures. We propose the temporal-adaptive fusion (TAF)-Net, a semisupervised dual-path framework that integrates a vision foundation model, MedSAM, and a task-specific UNet to leverage anatomical priors and fine-grained vascular features. To address interframe inconsistency and vessel discontinuity, we introduce a TAF strategy that dynamically fuses model predictions based on framewise confidence and temporal priors. In addition, we design a spatiotemporal topology-aware loss to enforce structural continuity by penalizing critical disconnection components across frames. Extensive experiments on two public multiframe DSA datasets (DIAS and DSCA) demonstrate that the TAF-Net consistently outperforms state-of-the-art methods in both overlap accuracy (DSC, IoU) and topological integrity (Cost, 95HD), especially under low-label regimes.