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在MRI上进行多标签关节细分的LG-nnU-net:在患有管的患者中进行定量评估

Xiuxiang Liu¹, Hua Ren¹, Jiahui Lv²

¹Department of Radiology, Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing, China.

European journal of radiology

|November 13, 2025

概括

一个新的深度学习框架,LG-nnU-net,在MRI上准确地细分关节子结构. 该工具提供了强大的定量解剖信息,用于在管患者的手术前评估.

科学领域:

医疗成像医学成像
人工智能在医学中的应用
解剖学的细分解剖学的细分

背景情况:

精确的关节子结构的细分对于理解尾的解剖学至关重要.
目前的细分方法可能缺乏稳定性和定量精度.

研究的目的:

开发和评估LG-nnU-net,这是一个深度学习框架,用于MRI上关节子结构的多标签细分.
为了提供强大的定量解剖信息,用于尾的评估.

主要方法:

对272名部囊患者进行T2加权MRI的回顾性研究.
开发LG-nnU-net,一个优化的nnU-net架构.
使用子相似系数 (DSC),豪斯多夫距离 (HD) 和平均对称表面距离 (ASSD) 进行LG-nnU-net与ResU-net,DenseU-net和U-net++的比较.

主要成果:

对于单个肌肉 (LAM,PRM,EAS) 和组合结构来说,LG-nnU-net表现出卓越的细分精度.
关节综合体的整体多结构细分实现了81.34%的DSC,超过了对比模型.
在DSC,HD和ASSD指标上,LG-nnU-net的表现优于其他模型.

结论:

该LG-nnU-net框架提供了MRI上关节子结构的准确和强大的细分.
结果表明,在尾患者的定量分析和手术前评估中具有潜在的实用性.
需要进一步的多中心验证和前性研究来验证对管分类的临床影响.

关键词:

门的塞.关节管细分关节细分在LG-nnU-net中使用.多标签的MRI细分.盆地底的解剖学盆地底的解剖学

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