基于连接性的皮层分片通过对比学习在空间图形卷积上进行
Peiting You1,2, Xiang Li1, Fan Zhang3
1Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
BME frontiers
|October 18, 2023
概括
这项研究引入了使用空间图表表示学习进行大脑分片的新框架. 开发的空间图形卷积分区 (SGCP) 方法在基于结构连接的脑区域绘制图表方面表现出卓越的性能.
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:
- 对于神经科学来说至关重要的皮层分片已经通过扩散成像和曲谱学得到了推进.
- 由于简单的计算方案和不充分的脑成像数据表示,以前的方法面临局限性.
研究的目的:
- 开发和评估一种新的皮层分片框架,使用导管学衍生的结构性大脑连接.
- 通过使用先进的空间图表表示学习来解决现有方法的局限性.
主要方法:
- 空间图形卷积分区 (SGCP) 框架使用了两阶段的深度学习方法.
- 第一阶段涉及自主监督的对比学习,使用空间图卷积网络编码器进行数据嵌入.
- 第二阶段采用监督分类器来对voxel进行分类,以实现分片化.
主要成果:
- 在分离5个大脑区域时,使用15个DWI数据集评估了SGCP.
- 与传统和其他基于深度学习的分片方法相比,该框架表现出了优异的性能.
- 在所有测试的分片任务中观察到一致的高性能.
结论:
- 拟议的SGCP框架为皮层分片提供了一个强大而有效的解决方案.
- 它的强大性能表明,它有可能成为使用连接数据研究人类大脑组成的通用化工具.
- SGCP在神经科学研究和医学图像分析中推进了表示学习的应用.
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