零样本评估SAM模型在CT骨分割中的表现，给出最佳提示策略
Zero-shot capability of SAM-family models for bone segmentation in CT scans
- 用边界框+中心点组合提示，实现零样本骨分割
- SAM和SAM2在不同数据集上表现差异大，最优策略因模型而异
- 为临床2D图像分割提供可落地的提示指南，适合医疗影像研究者
Segment Anything Model（SAM）及其同类模型属于可提示的基础模型（FMs），用于图像与视频分割。目标通过边界框或点等提示方式识别。随着这些模型进入医学图像分割领域，亟需在临床场景下开展全面评估以明确其优劣。由于性能高度依赖提示策略，必须研究不同提示方法以制定有效使用规范。目前尚无针对CT扫描中骨分割的专门评估研究，存在明显空白。因此，我们采用非迭代、'最优'的提示策略（包括边界框、点及组合），测试SAM家族模型在三个不同骨骼区域上的零样本骨分割能力。结果表明，最佳设置取决于模型类型与规模、数据集特征以及优化目标。总体而言，在所有测试条件下，使用边界框结合中心点提示所有物体组件时，SAM和SAM2表现最佳。鉴于结果受多重因素影响，本文提供基于非交互式'最优'提示的2D提示决策指南。
原文摘要 · Abstract (English)
The Segment Anything Model (SAM) and similar models build a family of promptable foundation models (FMs) for image and video segmentation. The object of interest is identified using prompts, such as bounding boxes or points. With these FMs becoming part of medical image segmentation, extensive evaluation studies are required to assess their strengths and weaknesses in clinical setting. Since the performance is highly dependent on the chosen prompting strategy, it is important to investigate different prompting techniques to define optimal guidelines that ensure effective use in medical image segmentation. Currently, no dedicated evaluation studies exist specifically for bone segmentation in CT scans, leaving a gap in understanding the performance for this task. Thus, we use non-iterative, ``optimal'' prompting strategies composed of bounding box, points and combinations to test the zero-shot capability of SAM-family models for bone CT segmentation on three different skeletal regions. Our results show that the best settings depend on the model type and size, dataset characteristics and objective to optimize. Overall, SAM and SAM2 prompted with a bounding box in combination with the center point for all the components of an object yield the best results across all tested settings. As the results depend on multiple factors, we provide a guideline for informed decision-making in 2D prompting with non-interactive, ''optimal'' prompts.
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