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使用基于反转的预处理进行MRI注释,用于CT模型适应

Hartmut Häntze^1,2, Lina Xu¹, Maximilian Nikolas Rattunde¹

¹Department of Radiology, Charité-Universitätsmedizin Berlin corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany.

European radiology experimental

|September 20, 2025

概括

计算机断层扫描 (CT) 细分模型可以生成准确的磁共振成像 (MRI) 预细分. 图像逆转预处理提高了CT模型在T1加权MRI上的性能,提高了结构和脏瘤的细分精度.

科学领域:

医学成像分析分析医学成像分析
放射学中的人工智能
跨模式图像细分跨模式图像细分

背景情况:

在MRI中手动注释新类是耗时的.
CT细分模型可以支持MRI分析,但直接翻译具有挑战性.
经过CT训练的模型可以创建精确的MRI预分割,有或没有图像反转.

研究的目的:

在MRI数据上评估CT训练的细分模型的性能.
评估图像反转预处理对细分精度的影响.
确定CT模型对各种解剖结构和脏瘤的MRI的概括性.

主要方法:

从100名患者的100个T1加权和100个T2加权的脂肪和MRI序列的回顾性分析.
应用一个通用的多类CT模型 (TotalSegmentator) 和一个专门的脏瘤模型.
在原始和强度反转的序列上使用子相似系数 (DSC) 评估细分质量.

主要成果:

分段精度因MRI序列和解剖结构而异.
CT模型在T2wfs序列 (DSC 0.60) 中准确地细分了脏,但在血管和肌肉方面遇到了困难.
强度逆转显著改善了T1w序列上的TotalSegmentator性能 (平均DSC为0.04至0.56,p <0.001).
逆转改善了T1w序列中的瘤细分DSC,从0.04到0.42 (p < 0.001).

结论:

经过CT训练的模型可以通过适当的预处理,如图像反转,将其推广为MRI.
逆向预处理使T1wMRI使用CT模型实现了细胞癌的细分.
CT模型显示了对MRI的潜在可转移性,加速了用于MRI分析的AI开发.

关键词:

人工智能的人工智能瘤 (细胞) 是一种瘤.图像处理 (计算机辅助)磁共振成像技术磁共振成像技术断层扫描 (X射线计算) 的使用

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