菲伊斯塔:用于精确的纤维细分的自编码器在曲谱学中
Félix Dumais1, Jon Haitz Legarreta2, Carl Lemaire3
1Sherbrooke Connectivity Imaging Lab (SCIL), Department of Computer Science, Université de Sherbrooke, Canada; Videos & Images Theory and Analytics Lab (VITAL), Department of Computer Science, Université de Sherbrooke, Canada.
NeuroImage
|July 26, 2023
概括
FIESTA (使用自动编码器进行 Tractography 中的 FIbEr 分段) 为大脑连接性研究提供自动化白质捆绑分段. 这种深度学习方法提高了轨道图的可靠性和覆盖范围,优于现有的方法.
科学领域:
- 神经成像是一种神经成像.
- 计算神经科学是一种神经科学.
- 医学图像分析 医学图像分析
背景情况:
- 白质束细分对于理解大脑的结构连接性至关重要.
- 当前的曲谱学方法在准确细分和回收所有白质束时面临着挑战.
研究的目的:
- 介绍FIESTA (使用自动编码器进行 Tractography 中的 FIbEr 分段),这是一个用于白质束分段的新型自动化管道.
- 为了提高在扩散MRI通道图中捆绑细分的可靠性,稳定性和覆盖率.
主要方法:
- 使用具有对比学习的深度自动编码器来建模流线的潜在空间.
- 使用生成采样和潜空间播种来回收难以追踪的捆绑.
- 基于自编码器隐藏距离对MNI空间中的捆绑图谱的细分曲线图.
主要成果:
- 通过生成解剖学上正确的,新的流线,FIESTA证明了提高了主体内捆绑可靠性.
- 与最先进的自动化虚拟解剖技术相比,该方法显示出更高的性能.
- 实现了增强的空间覆盖和恢复具有挑战性的白质束.
结论:
- 菲斯塔提供了一个可靠,自动化和适应性的框架,用于白质捆绑细分.
- 该管道增强了神经系统疾病,神经外科和衰老研究的通道图分析的实用性和可用性.
- 在不同的解剖捆定义之间提供灵活的过渡,最小的校准.
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