分段任何模型 (SAM) 和医学SAM (MedSAM) 用于腰椎脊柱MRI.
Christian Chang1, Hudson Law2, Connor Poon3
1Punahou School, Honolulu, HI 96822, USA.
Sensors (Basel, Switzerland)
|June 27, 2025
概括
像MedSAM这样的零射击人工智能模型对腰椎MRI扫描进行细分有前途,为腰部疼痛研究提供更快的适应性,尽管无法匹配nnU-Net的性能.
科学领域:
- 放射学和医学成像学 医学成像学
- 医疗保健中的人工智能
- 生物医学工程 生物医学工程
背景情况:
- 腰椎磁共振成像 (MRI) 对于评估脊椎间盘 (IVD) 和脊椎体 (VB) 在腰部疼痛中至关重要.
- 精确的IVD和VB细分提供了对形状和体积的定量见解,有助于诊断和研究.
- 当前的细分方法可能是劳动密集型的,需要大量的培训数据.
研究的目的:
- 评估"零射击"深度学习模型,分段任何模型 (SAM) 和医疗SAM (MedSAM) 的性能,用于在MRI上对腰椎IVD和VB进行细分.
- 将SAM和MedSAM的细分精度与已建立的nnU-Net模型进行比较.
- 评估在腰部疼痛研究中使用零射击模型进行定量分析的可行性.
主要方法:
- 一个尸体研究利用82个捐赠者脊柱与腰部MRI扫描.
- 由专家执行的手动细分服务于地面真相.
- 两位读者使用MRIROI上的点和边界框注释应用了SAM和MedSAM.
- 使用Dice得分,灵敏度和特异性的定量比较与地面真相和nnU-Net.
主要成果:
- 与SAM (0.64为IVD,0.83为VB) 相比,MedSAM显示了更高的平均子得分 (0.79为IVD,0.88为VB),具有统计学上显著的差异 (p < 0.001).
- 无论是SAM还是MedSAM,其性能都低于nnU-Net (0.99对于IVD和VB).
- MedSAM提供了比SAM更一致的细分结果,敏感度值也有利于MedSAM.
结论:
- 零射击深度学习模型,特别是MedSAM,可用于细分腰椎MRI,提供快速适应的潜力.
- 虽然目前的零射击模型与nnU-Net等专业模型的性能不匹配,但它们不需要培训数据是一个显著的优势.
- 进一步验证和开发用于腰椎MRI的可通用细分模型,可以提高诊断,患者随访和腰部疼痛研究,并通过自动化分析降低成本.
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