Probabilistic Brain Tumor Segmentation for Everyone

Motivation: Probabilistic segmentation offers notable advantages in the context of brain tumor delineation on MRI. However, existing methods are not accessible to most medical institutes, rendering their clinical relevance questionable. Goal(s): we present a foundation model-based probabilistic brain tumor segmentation approach designed 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 output the tumor mask and uncertainty for the brain tumor segmentation task. Results: The proposed method achieves a competitive performance with training on only five cases. It has the potential for a substantial impact on clinical practice. Impact: The proposed foundation model-based probabilistic brain tumor segmentation method is open-source and achieves a competitive performance with only five training cases. Such characteristics make it a valuable asset for healthcare institutions that have difficulties developing their proprietary probabilistic segmentation models.