在8421名急性缺血性中风患者中,MRI白质超强度的自动细分
Hosung Kim1, Wi-Sun Ryu2,3, Dawid Schellingerhout4
1From the USC Stevens Neuroimaging and Informatics Institute (H.K.), Keck School of Medicine of USC, University of Southern California, Los Angeles, California.
AJNR. American journal of neuroradiology
|July 16, 2024
概括
这项研究开发了深度学习模型,以准确地对脑梗塞患者的白质超强度 (WMH) 病变进行细分. SE-UNet模型表现出高性能,具有不确定性指数来识别需要人类审查的病例.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 神经学 神经学
背景情况:
- 由于病变边界模糊,脑梗塞中白质强度 (WMH) 病变的准确细分具有挑战性.
- 对于中风患者的WMH细分现有的深度学习研究的范围和数据大小有限.
研究的目的:
- 开发和验证深度学习算法,用于在急性缺血性中风患者中精确的WMH病变细分.
- 使用多站点数据评估2D UNet和SE-UNet模型的性能.
主要方法:
- 在3家医院的2408个FLAIRMRI上训练了2DUNet和SE-UNet模型.
- 在6家医院的6013个FLAIRMRI上验证了模型,总共8421名患者.
- 使用子相似系数 (DSC),相关系数和一致性相关系数来评估性能.
主要成果:
- 与UNet (0.710) 相比,SE-UNet在外部验证中实现了更高的平均DSC (0.722),接近人类可靠性 (0.744).
- 在自动和手动WMH体积细分之间观察到强烈的相关性 (r=0.933对于SE-UNet).
- 一个不确定性指数确定了细分不那么准确的案例,其中86%的外部案例的指数<0.35.
结论:
- 深度学习算法,特别是SE-UNet,可以使用大型数据集准确地对急性脑梗塞患者的WMH进行细分.
- 不确定性指数是识别需要人类审查的WMH细分案例的宝贵工具.
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