Breaking the Data Barrier: Robust Few-Shot 3D Vessel Segmentation using Foundation Models

TL;DR

This paper introduces a novel framework that adapts pre-trained vision foundation models for 3D vessel segmentation, significantly improving performance in few-shot and out-of-distribution scenarios with limited annotated data.

Contribution

The paper presents a lightweight 3D adapter, multi-scale 3D aggregator, and Z-channel embedding to effectively adapt foundation models for volumetric vessel segmentation, addressing data scarcity and domain shift challenges.

Findings

Achieved 43.42% Dice score with only 5 training samples, a 30% improvement over nnU-Net.

Demonstrated 50% relative improvement in out-of-distribution robustness over nnU-Net.

Validated effectiveness of the proposed adaptation mechanisms through ablation studies.

Abstract

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.