在皮层表面重建中的顶点对应和自我交叉减少.
IEEE transactions on medical imaging
|April 18, 2025
概括
我们开发了Vox2Cortex with Correspondence (V2CC),以改善大脑表面重建和顶点对应. 我们的方法增强了群组比较,并减少了网状自我交叉点,以便进行准确的神经成像分析.
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:
- 基于网状的皮质表面重建对于大脑形态分析至关重要.
- 准确的顶点对应对于组级比较至关重要,但传统上需要大量的后处理.
- 深度学习方法已经进行了先进的重建,但往往忽视了顶点对应优化.
研究的目的:
- 引入Vox2Cortex与对应 (V2CC) 以改善皮层表面重建中的对象内部和内部顶点对应.
- 在重建过程中解决和减少网格自我交叉,特别是主要类型.
- 为了提高重建网格的适用性,用于直接的群组比较和基于地图的分片.
主要方法:
- 通过在注册表面上用L1损失取代Chamfer损失来修改Vox2Cortex.
- 引入了一种新的自我近距离损失,通过调整非邻近顶点来减轻主要网格自我交叉点.
- 为了有针对性的分析,将网格自我交叉点分为次要和主要类型.
主要成果:
- 与现有的深度学习方法相比,V2CC显著改善了主体间和主体内顶点对应.
- 拟议的自我近距离损失有效地减少了主要网状网的自我交叉点.
- 实现了准确的对应,并将和白质表面的自我交叉减少到1%以下,提高了分片的准确性.
结论:
- 在皮层表面重建中,V2CC为准确的顶点对应提供了一个强大的解决方案.
- 该方法增强了神经成像数据的实用性,用于小组研究和基于图谱的分析.
- 这项工作解决了当前用于大脑表面分析的深度学习方法的关键局限性.
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