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深度学习辅助检测和分段子宫纤维瘤使用多方向磁共振成像使用的子宫纤维瘤

Xin-Yu Liu¹, Zhi-Lin Yuan¹, Fu-Ze Cong¹

¹Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, People's Republic of China.

Abdominal radiology (New York)

|April 5, 2025

概括

深度学习模型从多方向MRI扫描中准确地检测和细分子宫纤维瘤. 这些人工智能工具对诊断子宫纤维瘤的临床应用有前途.

科学领域:

医疗成像医学成像
人工智能的人工智能
在瘤学瘤学.

背景情况:

子宫纤维瘤是常见的良性瘤,影响生育年龄的女性.
精确检测和细分纤维瘤对于治疗规划和监测至关重要.
目前的手动细分方法耗时,并且受观察者之间的变化影响.

研究的目的:

开发和验证用于子宫纤维瘤自动检测和细分的深度学习模型.
通过使用多方向T2加权MRI扫描来评估这些模型的性能.
评估模型在不同纤维瘤大小和位置的有效性.

主要方法:

使用3D nnU-Net框架进行模型构建.
采用了从患有子宫纤维瘤的患者的斜腰和轴心T2加权MRI扫描.
通过回忆和精确度评估检测;用子相似系数 (DSC),豪斯多夫距离 (HD95) 和平均表面距离 (ASD) 评估细分.

主要成果:

模型实现了高精度 (97.9%-98.9%) 和良好的回忆 (74.4%-76.4%) 纤维瘤检测.
对于较大的纤维瘤 (≥4厘米),观察到优异的回忆 (93.7% - 95.3%).
在内部和外部数据集中,细分性能显示了0.789-0.834的平均DSC和低距离 (HD95:9.971-11.874毫米,ASD:2.031-2.273毫米).

结论:

深度学习模型显示了自动化子宫纤维素检测和细分的巨大潜力.
开发的模型为临床使用提供了可靠和高效的工具.
这些人工智能驱动的方法可以特别有助于识别临床相关的子宫纤维瘤.

关键词:

自动化细分系统自动化细分系统深度学习是一种深度学习.这就是为什么MRI是MRI.在子宫纤维瘤.在 nnU-Net 网络上.

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