SynPo: Boosting Training-Free Few-Shot Medical Segmentation via High-Quality Negative Prompts

TL;DR

SynPo enhances training-free few-shot medical image segmentation by improving negative prompt quality through a confidence map synergy module, achieving results comparable to training-based methods.

Contribution

The paper introduces SynPo, a novel method that improves negative prompt quality in training-free LVM-based segmentation using a confidence map synergy module.

Findings

SynPo achieves state-of-the-art performance on medical segmentation tasks.

The confidence map synergy module effectively selects high-quality prompts.

SynPo performs comparably to training-based few-shot segmentation methods.

Abstract

The advent of Large Vision Models (LVMs) offers new opportunities for few-shot medical image segmentation. However, existing training-free methods based on LVMs fail to effectively utilize negative prompts, leading to poor performance on low-contrast medical images. To address this issue, we propose SynPo, a training-free few-shot method based on LVMs (e.g., SAM), with the core insight: improving the quality of negative prompts. To select point prompts in a more reliable confidence map, we design a novel Confidence Map Synergy Module by combining the strengths of DINOv2 and SAM. Based on the confidence map, we select the top-k pixels as the positive points set and choose the negative points set using a Gaussian distribution, followed by independent K-means clustering for both sets. Then, these selected points are leveraged as high-quality prompts for SAM to get the segmentation results. Extensive experiments demonstrate that SynPo achieves performance comparable to state-of-the-art training-based few-shot methods.