基于超声波的状肌面积测量:自动与手动细分的可比性.
Alberto Guzzi1, Romina Ledergerber2, Oliver Faude2
1Department of Sport, Exercise and Health, University of Basel, Grosse Allee 6, 4052, Basel, Switzerland. alberto.guzzi@unibas.ch.
Journal of imaging informatics in medicine
|February 12, 2026
概括
这项研究引入了一种自动化工具,用于在超声波图像中对状肌横截面区域进行细分,为手工方法提供可靠和高效的替代方案. 开发的开源工具表现出良好的一致性和快速的分析时间,有助于临床和研究应用.
科学领域:
- 生物医学工程 生物医学工程
- 医学成像分析 医学成像分析
- 肌肉骨超声波 肌肉骨超声波
背景情况:
- 对于肌肉解剖横截面积 (ACSA) 分段的现有工具不是开源或同行评审的.
- 特别需要一个开源的,验证的工具来进行骨肌ACSA分析.
- 超声波成像对于肌形态的非侵入性评估至关重要.
研究的目的:
- 在超声波图像中开发和评估状肌ACSA的自动细分方法.
- 与手动细分相比,评估自动方法的可靠性和准确性.
- 为在研究和临床环境中提供快速,独立于操作者的工具,用于状肌分析.
主要方法:
- 从30名参与者身上获得了状肌的超声图像,这些人处于肌长度的25%,50%和75%.
- 进行了ACSA的手动细分,以评估内部和会话间的可靠性.
- 在497张图像上训练了三个神经网络,以比较手动和自动细分,评估ICC,SEM和MAE等准确度指标.
主要成果:
- 在手动细分方面,观察到良好的内部评价可靠性 (ICC=0.804) 和优秀的会话间可靠性 (ICC=0.980).
- 自动细分显示了与手动分析的良好可比性 (ICC=0.848,MAE=0.05cm2),分析时间为每张图像0.302-0.414秒.
- 在删除错误预测后,在手动和自动细分之间发现了一个小的标准化平均差异 (0.53).
结论:
- 提议的自动方法提供了一种快速且不太依赖于操作者的方法,用于骨ACSA分析.
- 虽然与手动分析相比存在小差异,但如果谨慎使用,该工具可以为临床和研究应用提供有价值的支持.
- 这种开源工具解决了被验证的形肌细分的缺口,有可能推进肌肉骨研究.
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