在过去的二十年中,白质超强度细分方法和实施的演变;向深度学习的不完全转变
Maryam Rahmani1,2, Donna Dierker1,2, Lauren Yaeger
1Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA.
Brain imaging and behavior
|July 31, 2024
概括
本综述分析了超过20年的白质超强度 (WMH) 分段方法. 深度学习越来越多地被使用,但视觉尺度仍然存在,突出显示了WMH研究中标准化的需要.
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
- 系统审查是系统的审查.
背景情况:
- 白质超强度 (WMH) 是神经和精神疾病中的关键生物标志物.
- 了解WMH患病率和细分技术对于疾病研究至关重要.
- 现有的文献显示了WMH分析的各种方法.
研究的目的:
- 从2000年到2022年,系统地审查WMH细分文献.
- 根据方法,流行和实施来对WMH工具进行分类.
- 识别WMH细分技术中的趋势和差异.
主要方法:
- 按照PRISMA指南进行系统的文献搜索.
- 基于方法的WMH细分工具的分类.
- 包括具有公开可用的技术和WMH作为主要结果的文章.
主要成果:
- 分析了1007个视觉评级表,118个管道和509个实施文章.
- 主要群体:衰老,痴呆,精神疾病,小血管疾病.
- 深度学习是最发达的细分技术;SPM是最常用的定量方法.
结论:
- 深度学习显示WMH细分的发展有所增加.
- 尽管有先进的定量方法,视觉评级表仍然很普遍.
- 为未来的研究,建议对WMH细分技术进行标准化.
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