零样本用SAM2自动分割3D CT，无需训练即可实现连贯三维分割。
Automatic Segmentation of 3D CT scans with SAM2 using a zero-shot approach
- 将CT切片视为有序序列，利用视频记忆机制适配3D数据
- 在2500例CT数据上达成连贯三维分割，无需微调
- 适合医疗影像领域快速部署，无需标注数据
图像分割的基础模型在自然图像中展现出强大泛化能力，但在3D医学影像中的应用仍受限。本文研究了零样本使用分割一切模型2（SAM2）对体数据CT进行自动分割的可行性，不进行任何微调或领域特定训练。分析表明，SAM2的主要局限在于缺乏内在的体积感知能力。为此，我们提出一系列仅在推理阶段的架构与流程改进，通过将CT切片视为有序序列，使SAM2的视频级记忆机制适应3D数据。我们在TotalSegmentator数据集的500例CT扫描中进行系统消融实验，评估提示策略、记忆传播方案及多轮精修效果。基于此选择最优配置，并在包含2,500例扫描的更大样本上报告最终结果。结果显示，即使权重冻结，只要推理流程设计得当，SAM2仍可生成连贯的3D分割，验证了全零样本方法在体数据医学图像分割中的可行性。
原文摘要 · Abstract (English)
Foundation models for image segmentation have shown strong generalization in natural images, yet their applicability to 3D medical imaging remains limited. In this work, we study the zero-shot use of Segment Anything Model 2 (SAM2) for automatic segmentation of volumetric CT data, without any fine-tuning or domain-specific training. We analyze how SAM2 should be applied to CT volumes and identify its main limitation: the lack of inherent volumetric awareness. To address this, we propose a set of inference-alone architectural and procedural modifications that adapt SAM2's video-based memory mechanism to 3D data by treating CT slices as ordered sequences. We conduct a systematic ablation study on a subset of 500 CT scans from the TotalSegmentator dataset to evaluate prompt strategies, memory propagation schemes and multi-pass refinement. Based on these findings, we select the best-performing configuration and report final results on a bigger sample of the TotalSegmentator dataset comprising 2,500 CT scans. Our results show that, even with frozen weights, SAM2 can produce coherent 3D segmentations when its inference pipeline is carefully structured, demonstrating the feasibility of a fully zero-shot approach for volumetric medical image segmentation.
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