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How to Measure Cortical Folding from MR Images: a Step-by-Step Tutorial to Compute Local Gyrification Index
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使用深度无监督学习对皮层表面网格进行快速球形映射.

Fenqiang Zhao1, Zhengwang Wu1, Li Wang1

  • 1Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|June 16, 2023
PubMed
概括

我们开发了一种深度学习方法,用于脑皮层表面的球形绘制,显著减少扭曲并加快处理速度. 这种新的方法通过创建更准确和更有效的球形网状表示来增强神经成像分析.

科学领域:

  • 神经成像是一种神经成像.
  • 计算解剖学的计算解剖学
  • 医学图像分析 医学图像分析

背景情况:

  • 球形绘制对于在神经成像中进行皮质表面注册和分析至关重要.
  • 传统的方法会产生扭曲的球形网格,并且计算密集.
  • 现有的技术在平衡度量,面积和角度扭曲方面缺乏灵活性.

研究的目的:

  • 开发基于深度学习的算法,以准确有效地绘制皮质表面的球形绘图.
  • 克服传统代优化方法的局限性.
  • 创建一个灵活的框架,用于特定应用的网格生成.

主要方法:

  • 使用球体U-Net模型来学习球体二形态变形场.
  • 实施了一个端到端无监督学习方案,以实现灵活的优化.
  • 集成了一个粗到细的多重分辨率框架,以纠正细度扭曲.

主要成果:

  • 与FreeSurfer相比,深度学习方法显著减少了扭曲.
  • 每个表面的处理时间从20分钟缩短到5秒.
  • 在800多个皮质表面上得到验证,证明了卓越的性能.

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结论:

  • 拟议的深度学习算法为皮质表面球形映射提供了一个计算效率高,准确的解决方案.
  • 这种方法在最大限度地减少各种网格扭曲方面提供了更大的灵活性.
  • 这种方法有可能推进大规模的神经成像研究.