nnU-Net 基于肺栓塞的深度学习:在RSPECT数据集中的检测,凝块体积定量和严重程度相关性
Ezio Lanza1, Angela Ammirabile1, Marco Francone1
1Humanitas University, Department of Biomedical Sciences, Via Rita Levi Montalcini, 4, Pieve Emanuele MI 20072, Italy; IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, Milan 20089, Italy.
European journal of radiology
|July 5, 2024
概括
这项研究表明,nnU-Net深度学习算法准确检测肺栓塞 (PE) 和测量血凝块体积 (BCV). 这种AI工具有助于优先考虑PE病例并评估严重程度,即使对于没有AI专业知识的人来说也是如此.
科学领域:
- 医疗成像中的人工智能
- 深度学习用于诊断成像.
- 肺栓塞检测检测 肺栓塞检测
背景情况:
- CT肺血管造影是诊断肺栓塞 (PE) 的标准.
- 越来越多的需求需要像深度学习 (DL) 算法这样的高效诊断工具.
- nnU-Net框架简化了用于医学成像的DL模型开发.
研究的目的:
- 评估一个本地开发的nnU-Net算法用于PE检测.
- 评估算法测量血块体积 (BCV) 的能力.
- 在PE患者中,将BCV与右心室 (RV) 过载相关联.
主要方法:
- 在RSPECT数据集上训练了nnU-Net (205个PE,340个负面案例).
- 在6573次考试中进行了测试,分析了PE特性和RV过载.
- 提取的BCV和使用的ROC曲线和物流回归用于验证.
主要成果:
- 中位数BCV为负的1μL,PE阳性的345μL,中心PE的7,378μL.
- 在BCV,PE存在,中心PE和RV/LV比率 (p < 0.0001) 之间发现了显著的相关性.
- 检测PE的AUC为0.865 (83%的准确率),中央PE为0.937 (91%的准确率) 和VR过载为0.848 (79%的准确率).
结论:
- nnU-Net可以准确地检测PE,特别是中央PE.
- BCV是用于自动化严重程度分层和病例优先级的可靠指标.
- nnU-Net提供了一个用户友好的DL解决方案,用于PE检测和严重程度评估.
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