通过高质量负提示提升医学图像分割效果，无需训练即可实现高精度少样本分割。
SynPo: Boosting Training-Free Few-Shot Medical Segmentation via High-Quality Negative Prompts
- 利用DINOv2与SAM融合生成置信度图，精准选择正负提示点。
- 在低对比度医学图像上达到与有训练方法相当的分割性能。
- 适合需要快速部署、无标注数据场景的医疗图像分割应用。
大型视觉模型（LVMs）为少样本医学图像分割带来了新机遇。然而，现有基于LVMs的免训练方法未能有效利用负提示，导致在低对比度医学图像上表现不佳。为此，我们提出SynPo，一种基于LVMs（如SAM）的免训练少样本分割方法，核心思路是提升负提示质量。为更可靠地选择点提示，我们设计了结合DINOv2与SAM优势的置信度图协同模块。基于该置信度图，选取置信度最高的k个像素作为正样本点集，并用高斯分布选取负样本点集，再对两组点分别进行独立K-means聚类。最终将这些精选点作为高质量提示输入SAM，获得分割结果。大量实验表明，SynPo性能可媲美当前最先进的训练型少样本方法。
原文摘要 · Abstract (English)
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.
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