一个完全自动化的视觉分级系统,用于白物质T2-流体减弱反转恢复的超强度磁共振成像
ZunHyan Rieu1, Regina Ey Kim1, Minho Lee1
1Research Institute, NEUROPHET Inc., 06234 Seoul, Republic of Korea.
Journal of integrative neuroscience
|May 31, 2023
概括
这项研究介绍了在脑MRI上对白质超强度 (WMH) 的分级的自动化系统,为人类专家提供可靠和一致的评分.
科学领域:
- 神经成像是一种神经成像.
- 医学图像分析 医学图像分析
- 人工智能在医学中的应用
背景情况:
- 法泽卡斯尺度是对T2-流体减弱反转回收 (T2-FLAIR) MRI上的白质超强度 (WMH) 的分级标准.
- 目前WMH的视觉分级在可靠性方面存在局限性,并且是劳动密集型的.
- 需要自动化,可量化的方法来改善WMH评估.
研究的目的:
- 开发和验证一个完全自动化的WMH视觉分级系统.
- 提高脑成像中WMH评分的可靠性和效率.
- 为协助放射科医生进行临床评估提供一个公正的工具.
主要方法:
- 开发了一个四个阶段的自动化系统:WMH和心室的深度学习细分,分类为周周 (PWMH) 和深度 (DWMH) WMH,WMH直径测量和自动评分.
- 修改了可量化的评分方法,用于Fazekas等级.
- 将自动化系统的性能与使用T2-FLAIR图像从404个受试者的三个神经放射学家进行了比较.
主要成果:
- 该自动化系统的性能与神经放射学家的性能相当.
- 克里多夫的阿尔法表明,自动化系统和放射科医生之间存在实质性和中度的协议.
- 在放射科医生和自动化方法之间,接收器操作特征曲线下的平均面积类似.
结论:
- 完全自动化的WMH视觉分级系统显示性能与人类放射科医生相美.
- 这种自动化系统有可能通过提供公正和一致的临床评分来帮助放射科医生.
- 开发的系统为临床实践中的WMH评估提供了可靠的替代方案.
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