用轻量3D模块让预训练模型高效适应少标注的血管分割任务。

State-of-the-art vessel segmentation methods typically require large-scale annotated datasets and suffer from severe performance degradation under domain shifts. In clinical practice, however, acquiring extensive annotations for every new scanner or protocol is unfeasible. To address this, we propose VesselBridge3D, a foundation model adaptation framework that bridges frozen vision foundation models and volumetric vessel segmentation through lightweight 3D adaptation modules. The framework combines a lightweight 3D Adapter, a multi-scale 3D Aggregator, and Z-channel embedding for efficient adaptation to volumetric medical images. We instantiate VesselBridge3D with three frozen foundation encoders (DINOv3, MedSAM, and MedGemma) and evaluate it on the TopCoW (ID) and Lausanne (OOD) datasets. In the extreme low-data regime with 5 training samples, our method achieved a Dice score of 43.42%, marking a 30% relative improvement over the state-of-the-art nnU-Net (33.41%) and outperforming other Transformer-based baselines by up to 45%. The proposed framework was effective across all evaluated frozen foundation encoders, with DINOv3 yielding the best performance in the most label-efficient settings. Furthermore, in the out-of-distribution setting, our model demonstrated superior robustness, achieving a 50% relative improvement over nnU-Net (21.37% vs. 14.22%), which suffered from severe domain overfitting. Ablation studies confirmed the effectiveness of the proposed 3D adaptation modules. Our results demonstrate that VesselBridge3D is an effective framework for label-efficient 3D vessel segmentation under data scarcity and domain shifts.