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自动细分扩展肌和检测肌与卷积神经网络的肌

Mathias Hämäläinen¹, Markus Sormaala¹, Tuomas Kaseva¹

¹Department of Radiology, HUS Diagnostic Center, Helsinki University Hospital and University of Helsinki, and Jorvi Hospital, Espoo, Finland.

Acta radiologica open

|December 3, 2024

概括

卷积神经网络 (CNN) 可以在手腕MRI中自动检测延伸 (ECU) 肌和肌. 这种深度学习方法为诊断手腕疼痛的常见原因提供了有效的解决方案.

科学领域:

医疗成像医学成像
人工智能在医学中的应用
肌肉骨放射学肌肉骨放射学

背景情况:

延伸性 (ECU) 肌痛是慢性手腕疼痛的常见原因,需要准确及时诊断.
在MRI中自动检测和细分ECU肌和肌,可以帮助预防患者的残疾.
本研究探讨了深度学习在手腕MRI中用于肌肉骨结构分析的应用.

研究的目的:

开发和评估一个卷积神经网络 (CNN) 用于自动检测和细分ECU肌.
为了能够在2D轴心手腕MRI中自动划分由T2高强度病变识别的肌.
为了证明在MRI中自动化肌肉骨细分的可行性,以有效检测肌.

主要方法:

对1081个手腕MRI扫描进行了回顾性分析,其中46名患者被诊断患有肌病.
开发了两种基于深度学习的方法来细分ECU肌和肌病变.
训练和评估的方法是使用二维轴向手腕MRI系列上的Dise得分.

主要成果:

对于ECU肌细分的平均子得分从0.61到0.64不等.
肌病检测在值和CNN方法中获得0.38的子得分.
开发的方法表明,肌病的检测率为72%-76%.

结论:

开发的CNN在2DMRI中有效地检测和细分了ECU肌.
在CNN和信号强度值方法之间,肌病检测的准确性是可比的.
这项研究是使用深度学习进行自动化肌肉骨MRI分析的概念验证.

关键词:

在MR-成像中使用MR成像.计算机应用程序-检测检测诊断诊断诊断诊断诊断诊断细分化细分化的细分化肌肌肌肌是一个他们的手腕.

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