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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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SegCSR:从大脑皮带细分的弱监督皮质表面重建.

Hao Zheng1, Xiaoyang Chen1, Hongming Li1

  • 1Department of Radiology, The Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104, USA.

bioRxiv : the preprint server for biology
|December 23, 2024
PubMed
概括

这项研究引入了一种新的弱监督的深度学习方法,用于使用大脑MRI细分进行皮质表面重建 (CSR). 该方法准确地重建多个皮质表面,克服传统方法的局限性.

关键词:
脑部的核磁共振成像皮质表面的重建,皮质表面的重建.深度学习是一种深度学习.监管能力较弱 监管能力较弱

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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
  • 计算神经科学是一种神经科学.

背景情况:

  • 对于皮层表面重建 (CSR) 的深度学习通常需要伪地面真相 (pGT) 数据.
  • 这种对pGT的依赖导致了数据集特定的问题和广泛的数据准备.

研究的目的:

  • 开发一种弱监督的深度学习方法,从脑MRI中重建多个皮质表面.
  • 克服传统监管企业社会责任方法的局限性.

主要方法:

  • 初始化一个中厚的表面,并使用学习的不同形态流动将其变形为内 (白质) 和外 () 表面.
  • 使用边界表面损失来使表面与细分地图边界对齐.
  • 利用表面间正常的一致性损失在深中进行调整.
  • 整合表面光滑性和拓学的规范化.

主要成果:

  • 弱监督方法实现了与监督深度学习替代方案相比或优于企业社会责任的准确性.
  • 在两个大规模的大脑MRI数据集上进行了评估,证明了强大的性能.
  • 该方法显示皮质表面重建的规律性得到改善.

结论:

  • 弱监督学习为皮质表面重建提供了一个可行的和有效的替代方案.
  • 这种方法减少了对pGT的依赖,简化了数据准备,提高了概括性.
  • 拟议的方法为神经成像研究提供了准确和定期的皮质表面重建.