Breaking the Data Barrier: Robust Few-Shot 3D Vessel Segmentation using Foundation Models
This addresses the challenge of data scarcity and domain shifts in clinical medical imaging, offering a cold-start solution for vessel segmentation, though it is incremental in adapting foundation models to a specific domain.
The paper tackles the problem of 3D vessel segmentation with limited annotated data and domain shifts by adapting a pre-trained Vision Foundation Model (DINOv3) with novel 3D components, achieving a 30% relative improvement in Dice score over state-of-the-art methods with only 5 training samples and 50% better robustness in out-of-distribution settings.
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 a novel framework leveraging a pre-trained Vision Foundation Model (DINOv3) adapted for volumetric vessel segmentation. We introduce a lightweight 3D Adapter for volumetric consistency, a multi-scale 3D Aggregator for hierarchical feature fusion, and Z-channel embedding to effectively bridge the gap between 2D pre-training and 3D medical modalities, enabling the model to capture continuous vascular structures from limited data. We validated our method on the TopCoW (in-domain) and Lausanne (out-of-distribution) datasets. In the extreme few-shot 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, such as SwinUNETR and UNETR, by up to 45%. 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 that our 3D adaptation mechanism and multi-scale aggregation strategy are critical for vascular continuity and robustness. Our results suggest foundation models offer a viable cold-start solution, improving clinical reliability under data scarcity or domain shifts.