SAM在腹部CT的模拟域偏移下表现稳定，适合用于医疗数字孪生的解剖建模。
Robustness Evaluation of a Foundation Segmentation Model Under Simulated Domain Shifts in Abdominal CT: Implications for Health Digital Twin Deployment
- 用标准边界框隔离编码器性能，评估分割模型在扫描差异下的鲁棒性。
- 10种扰动下平均Dice下降不足0.01，失败率无显著上升。
- 结果支持将SAM作为医学图像分割的可靠基础模型，尤其适用于数字孪生应用。
基础分割模型如分割一切模型（SAM）在自然图像上表现出强泛化能力，但其在临床真实医学影像域偏移下的鲁棒性尚未充分量化。本研究对SAM（ViT-B）在腹部CT脾脏分割任务中进行了系统性的切片级鲁棒性评估，使用来自医学分割挑战赛（Medical Segmentation Decathlon）的41个体积共1,051个非空切片。采用基于真值的标准边界框协议，以分离编码器鲁棒性与提示不确定性。在十种条件下施加模拟跨扫描仪变异的控制扰动，包括高斯噪声、模糊、对比度缩放、伽马校正和分辨率不匹配。干净基线的平均Dice得分为0.9145（95%置信区间：[0.909, 0.919]），失败率为0.67%。所有扰动下绝对均值ΔDice低于0.01。经配对威尔科克森符号秩检验并校正假发现率后，在部分条件下检测到统计显著但幅度微小的变化，而麦内玛分析显示失败概率无显著增加。结果表明，SAM在中等程度的CT域偏移下仍保持稳定的分割行为，支持其作为医学图像分割研究的鲁棒基础模型。随着健康数字孪生越来越多地采用基础分割模型进行解剖建模与器官水平监测，对实际成像变异性下的鲁棒性进行正式表征，是实现可信部署的必要步骤。
原文摘要 · Abstract (English)
Foundation segmentation models such as the Segment Anything Model (SAM) have demonstrated strong generalization across natural images; however, their robustness under clinically realistic medical imaging domain shifts remains insufficiently quantified. We present a systematic slice-level robustness audit of SAM (ViT-B) for spleen segmentation in abdominal CT using 1,051 nonempty slices from 41 volumes in the Medical Segmentation Decathlon. A standardized ground-truth-derived bounding-box protocol was used to isolate encoder robustness from prompt uncertainty. Controlled perturbations simulating inter-scanner variability, including Gaussian noise, blur, contrast scaling, gamma correction, and resolution mismatch, were applied across ten conditions. The clean baseline achieved a mean Dice score of 0.9145 (95% CI: [0.909, 0.919]) with a failure rate of 0.67%. Across all perturbations, the absolute mean ΔDice remained below 0.01. Paired Wilcoxon signed-rank tests with Benjamini-Hochberg false discovery rate correction identified statistically significant but small-magnitude changes under selected conditions, while McNemar analysis showed no significant increase in failure probability. These findings indicate that SAM exhibits stable segmentation behavior under moderate CT domain shifts, supporting its role as a robust foundation baseline for medical image segmentation research. As health digital twins increasingly incorporate foundation segmentation models for anatomical modeling and organ-level monitoring, formal characterization of robustness under real-world imaging variability is a necessary step toward trustworthy deployment.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。