仅用一例标注即可精准分割腰椎旁肌，结果媲美专家水平。
nnSAM2: nnUNet-Enhanced One-Prompt SAM2 for Few-shot Multi-Modality Segmentation and Composition Analysis of Lumbar Paraspinal Muscles
- 用单张标注图生成伪标签，跨数据集迭代优化分割。
- 在多模态影像上达0.94-0.96的分割准确率，测量值与专家一致。
- 适合医学影像少样本分割，尤其适用于多中心研究场景。
目的：开发并验证nnsam2，在每数据集仅需单张标注切片的极小监督条件下，实现腰椎旁肌的少样本分割，并评估其在多序列MRI和多协议CT中与专家测量的统计可比性。方法：回顾性分析762名参与者共1,219例扫描（19,439切片）来自六个数据集。六张切片（每数据集一张）作为标注样本，其余19,433切片用于测试。nnsam2利用单切片SAM2提示生成伪标签，跨数据集聚合后通过三个独立nnU-Net模型逐级优化。使用骰子相似系数（DSC）评估分割性能，自动化测量包括肌肉体积、脂肪比率和CT衰减值，采用双单侧等效检验（TOST）和组内相关系数（ICC）进行评估。结果：nnsam2优于原始SAM2、其医疗变体、TotalSegmentator及领先少样本方法，在MR图像上DSC为0.94-0.96，在CT上为0.92-0.93。自动化与专家测量在肌肉体积（MRI/CT）、CT衰减、Dixon脂肪比率上统计等效（TOST，P < 0.05），ICC consistently 高达0.86-1.00。结论：nnsam2是一种先进的多模态少样本分割框架，生成的肌肉体积（MRI/CT）、衰减（CT）和脂肪比率（Dixon MRI）测量值与专家参考无统计差异。在多模态、多中心、跨国队列中验证，开源代码与数据，展现高标注效率、强泛化能力与可重复性。
原文摘要 · Abstract (English)
Purpose: To develop and validate No-New SAM2 (nnsam2) for few-shot segmentation of lumbar paraspinal muscles using only a single annotated slice per dataset, and to assess its statistical comparability with expert measurements across multi-sequence MRI and multi-protocol CT. Methods: We retrospectively analyzed 1,219 scans (19,439 slices) from 762 participants across six datasets. Six slices (one per dataset) served as labeled examples, while the remaining 19,433 slices were used for testing. In this minimal-supervision setting, nnsam2 used single-slice SAM2 prompts to generate pseudo-labels, which were pooled across datasets and refined through three sequential, independent nnU-Net models. Segmentation performance was evaluated using the Dice similarity coefficient (DSC), and automated measurements-including muscle volume, fat ratio, and CT attenuation-were assessed with two one-sided tests (TOST) and intraclass correlation coefficients (ICC). Results: nnsam2 outperformed vanilla SAM2, its medical variants, TotalSegmentator, and the leading few-shot method, achieving DSCs of 0.94-0.96 on MR images and 0.92-0.93 on CT. Automated and expert measurements were statistically equivalent for muscle volume (MRI/CT), CT attenuation, and Dixon fat ratio (TOST, P < 0.05), with consistently high ICCs (0.86-1.00). Conclusion: We developed nnsam2, a state-of-the-art few-shot framework for multi-modality LPM segmentation, producing muscle volume (MRI/CT), attenuation (CT), and fat ratio (Dixon MRI) measurements that were statistically comparable to expert references. Validated across multimodal, multicenter, and multinational cohorts, and released with open code and data, nnsam2 demonstrated high annotation efficiency, robust generalizability, and reproducibility.
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