DGRTA: Cross-Modality Unsupervised Domain Adaptation for Intracranial Vessel Segmentation via Dual-Gated Refinement and Topology-Aware Weighting

Abstract

TOF-MRA intracranial vessel segmentation is critical in clinical practice but challenged by limited annotations and significant cross-modality domain shifts. To address these issues, this study proposes DGRTA, an unsupervised domain adaptation framework that integrates cross-modality pseudo-label generation, a dual-gated pseudo-label refinement strategy (DGR), and a topology-aware weighting mechanism (TA). Initially, rigid and non-rigid registration are used to transfer CTA predictions to TOF-MRA to generate initial pseudo-labels. DGR then refines these labels using prediction probabilities and image intensity, enhancing sensitivity and specificity, while TA leverages Persistence Diagrams (PD) to quantify topological discrepancies and dynamically adjust loss weights. Experiments on 185 paired CTA/TOF-MRA cases demonstrated that DGRTA consistently improved performance across four backbone architectures (UNet, Attention UNet, UNETR, Swin UNETR). The Attention UNet-DGRTA achieved the best results, with a Dice of 0.810, clDice of 0.800, and an AHD of 0.353 mm on the validation set, significantly outperforming the baseline model (p < 0.001). DGRTA offers a feasible solution that reduces reliance on extensive manual annotations, underscoring the potential of unsupervised cross-modality segmentation in various vascular imaging applications.