无需训练即可精准分割息肉，通过动态调整阈值和权重提升分割精度。
RPG-SAM: Reliability-Weighted Prototypes and Geometric Adaptive Threshold Selection for Training-Free One-Shot Polyp Segmentation
- 基于可靠性加权原型挖掘，筛选高质量支持图像特征。
- 动态调整二值化阈值，使分割结果更贴合病灶形态。
- 适合医学图像分割场景，尤其适用于无标注数据的快速部署。
无训练的一次性分割为避免专家标注提供了可扩展的替代方案，知识通常从支持图像和基础模型中迁移而来。但现有方法往往对支持图像中的所有像素和查询响应强度进行同质处理，忽略了支持图像的区域异质性以及查询响应的异质性。为此，我们提出RPG-SAM框架，系统性地解决这些异质性差距。具体而言，为应对区域异质性，引入可靠性加权原型挖掘（RWPM），优先使用高保真支持特征，并以背景锚点作为对比参考以抑制噪声；为应对响应异质性，设计几何自适应选择（GAS），通过评估候选区域的形态一致性来动态校准二值化阈值。最后，采用迭代精炼环路以优化解剖边界。通过考虑多层次信息异质性，RPG-SAM在Kvasir数据集上实现了5.56%的mIoU提升。代码将公开。
原文摘要 · Abstract (English)
Training-free one-shot segmentation offers a scalable alternative to expert annotations where knowledge is often transferred from support images and foundation models. But existing methods often treat all pixels in support images and query response intensities models in a homogeneous way. They ignore the regional heterogeity in support images and response heterogeity in query.To resolve this, we propose RPG-SAM, a framework that systematically tackles these heterogeneity gaps. Specifically, to address regional heterogeneity, we introduce Reliability-Weighted Prototype Mining (RWPM) to prioritize high-fidelity support features while utilizing background anchors as contrastive references for noise suppression. To address response heterogeneity, we develop Geometric Adaptive Selection (GAS) to dynamically recalibrate binarization thresholds by evaluating the morphological consensus of candidates. Finally, an iterative refinement loop method is designed to polishes anatomical boundaries. By accounting for multi-layered information heterogeneity, RPG-SAM achieves a 5.56\% mIoU improvement on the Kvasir dataset. Code will be released.
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