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自动化深度学习方法用于全乳腺细分在无对比量的MRI中

Weibo Gao¹, Yanyan Zhang¹, Bo Gao¹

¹Department of Radiology, The Second Affiliated Hospital of Xi'an Jiaotong University, No. 157, West Fifth Road, Xincheng District, Xi'an, Shaanxi, 710004, China.

BMC medical imaging

|September 27, 2025

概括

nnU-Net深度学习模型使用扩散权重成像 (DWI) 和合成MRI (SyMRI) 实现了高度准确的全乳腺自动细分. 这一进步有助于高效分析大乳房MRI数据集.

科学领域:

医疗成像医学成像
人工智能的人工智能
放射学放射学是一门学科.

背景情况:

开发用于乳腺MRI的自动细分对于有效的临床分析至关重要.
扩散权重成像 (DWI) 和合成MRI (SyMRI) 提供了有价值的定量数据.
深度学习架构在医疗图像细分任务中表现有前途.

研究的目的:

开发和评估nnU-Net深度学习架构,以实现全自动化全乳腺细分.
将nnU-Net的性能与U-Net架构进行比较.
通过使用DWI和SyMRI数据来评估细分精度.

主要方法:

应用nnU-Net和U-Net深度学习算法对98名患者的196个乳房进行细分.
数据包括3.0TMRI扫描与DWI和SyMRI序列.
使用子相似系数 (DSC),准确性和皮尔森相关系数来量化性能.

主要成果:

在DWI和SyMRI (PD) 两种全乳腺细分方面,nnU-Net显著优于U-Net.
赛姆里 (PD) 显示出优于DWI的性能,实现了最高的DSC和精度.
在DWI和SyMRI (PD) 中,nnU-Net获得了优异的相关系数 (R2 0.991.00).

结论:

nnU-Net为使用无对比度定量MRI的全乳腺自动细分提供了卓越的性能.
这种自动化方法对于处理大型临床数据集是有效的.
这种方法代表了乳腺DWI和SyMRI的计算机辅助定量分析的重大进步.

关键词:

扩散权重成像技术的使用.合成磁共振成像技术合成磁共振成像技术这就是U-Net.整个乳房细分的细分.在 nnU-Net 网络上.

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