用SAM2零训练实现3D医学图像少样本分割，提升精度与效率
Rethinking Few-Shot Medical Image Segmentation by SAM2: A Training-Free Framework with Augmentative Prompting and Dynamic Matching
- 将3D医学图像视为视频序列，用增强支持图匹配查询帧
- 单张标注图经增强后生成多提示，动态选择最相似匹配
- 无需训练，即插即用，适合临床快速标注场景
医疗图像分割高度依赖大规模标注数据，现有少样本方法仍需大量训练。本文提出一种新方法，利用具备强视频分割能力的视觉基础模型SAM2。将3D医学图像体积视为视频序列，突破传统逐切片处理范式。核心创新为支持-查询匹配策略：对单张标注的支持图像进行大量数据增强，针对查询体积中每一帧，算法选择最相似的增强支持图像，以其掩码作为掩码提示，驱动SAM2进行视频分割。该方法完全避免模型重训练或参数更新。在多个基准少样本医学图像分割数据集上实现顶尖性能，显著提升准确率与标注效率。该即插即用方法为3D医学图像分割提供强大且通用的解决方案。
原文摘要 · Abstract (English)
The reliance on large labeled datasets presents a significant challenge in medical image segmentation. Few-shot learning offers a potential solution, but existing methods often still require substantial training data. This paper proposes a novel approach that leverages the Segment Anything Model 2 (SAM2), a vision foundation model with strong video segmentation capabilities. We conceptualize 3D medical image volumes as video sequences, departing from the traditional slice-by-slice paradigm. Our core innovation is a support-query matching strategy: we perform extensive data augmentation on a single labeled support image and, for each frame in the query volume, algorithmically select the most analogous augmented support image. This selected image, along with its corresponding mask, is used as a mask prompt, driving SAM2's video segmentation. This approach entirely avoids model retraining or parameter updates. We demonstrate state-of-the-art performance on benchmark few-shot medical image segmentation datasets, achieving significant improvements in accuracy and annotation efficiency. This plug-and-play method offers a powerful and generalizable solution for 3D medical image segmentation.
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