Handy Hepatic Veins Segmentation on MR Images using Foundation Models

Motivation: Accurate hepatic vessel segmentation can help to identify and avoid critical blood vessels during liver tumor ablation or resection. Existing methods are not accessible to most medical institutes, leading to questionable clinical relevance. Goal(s): we present a handy foundation model-based hepatic vessel segmentation approach crafted for straightforward integration into clinical applications. Approach: We employ a parameter-efficient few-shot learning strategy to fine-tune the foundation model, thereby enabling it to achieve competitive hepatic vessel segmentation performance with training on only five cases. Results: The proposed method is effective and easy-to-access, and it has the potential for a substantial impact on clinical practice. Impact: Existing hepatic vessel segmentation methods are not accessible to most medical institutes, leading to questionable clinical relevance. We present a clinically practical foundation model-based approach that achieves competitive performance with training on only five cases.