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基于磁共振成像的骨盆和部3维模型,使用机器学习进行动态部阻塞模拟的自动骨段化

Till D Lerch¹, Guodong Zeng², Adam Boschung³

¹Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, University of Bern, Bern, Switzerland.

Orthopaedic journal of sports medicine

|September 22, 2025

概括

使用MRI对3D部骨模型进行自动细分,与诊断股骨支骨 (FAI) 的手动方法一样准确. 这种深度学习方法可以实现有效的,针对患者的手术规划,用于部保护和关节镜手术.

科学领域:

医学成像分析分析医学成像分析
整形外科手术整形外科手术
医学中的人工智能

背景情况:

腿骨 (FAI) 可能导致部疼痛和骨关节炎.
来自动态部模拟的3D骨模型有助于患者特定的FAI诊断.
手动对骨模型进行细分是耗时的,需要自动化方法.

研究的目的:

比较基于MRI的3D部骨模型的手动和自动细分.
关联自动细分与无冲击的部运动范围.
验证自动细分的外部准确性.

主要方法:

98个部的回顾性研究 (30名FAI患者,19名无症状).
使用快速3D T1加权VIBE迪克森序列获得的3-T MRI.
利用机器学习 (CNN) 进行自动细分;计算子相似系数 (DSC) 和运动范围差异.

主要成果:

与手动相比,自动细分显示了最小的差异 (<1毫米) 和高的DSC (94.1%-97.5%).
对于无冲击曲的良好相关性 (r=0.93).
外部验证实现了92.2%-94.9%的DSC.

结论:

基于MRI的3D骨模型的自动细分对于FAI患者来说是准确的.
能够实现无辐射,患者特定的手术规划,用于部保护和关节镜手术.
通过常规的MRI和短的获取时间,深度学习细分是可行的.

关键词:

三维模型是3D模型.基于MRI的3D骨模型骨的细分骨的细分骨的细分关节关节镜外科手术关节镜手术部撞击发生在部.保护部的手术部保护手术机器学习是机器学习.

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