BAPM: Boundary-Aware Prompt Mining for Training-Free Few-Shot Medical Image Segmentation

Few-shot medical image segmentation has shown great potential in reducing annotation costs for clinical applications. Recently, many few-shot methods have explored the Segment Anything Model (SAM) for training-free medical image segmentation via prompt engineering. However, existing approaches mainly focus on locating positive prompts from support images while overlooking informative background cues, making them prone to over-segmentation in anatomically similar regions. Moreover, simply introducing negative prompts cannot effectively suppress boundary leakage, and may even interfere with SAM’s mask decoding process, resulting in worse performance than using positive prompts alone. To address this limitation, we propose a training-free support-query framework, termed Boundary-Aware Prompt Mining, which introduces boundary-aware negative prompting for SAM-based few-shot medical image segmentation. Specifically, we introduce a Prototype-guided Positive Prompt strategy, which adopts a multi-center prompting mechanism to construct multiple foreground prototypes, enabling comprehensive spatial coverage in query images. Furthermore, we propose a Boundary-band Ambiguity Filtering strategy that identifies a boundary-adjacent background band and progressively removes ambiguous pixels and unreliable clusters highly similar to the foreground, enabling the selection of reliable negative prompts for suppressing false positives near foreground boundaries. The generated positive and negative prompts are jointly fed into SAM to produce refined segmentation results without additional training. Extensive experiments on Abd-MRI and Abd-CT datasets demonstrate that our method consistently outperforms existing approaches, highlighting its effectiveness and robustness under limited annotation settings. Code will be released upon acceptance.