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引用次数: 2

摘要

脑形态测量学在神经影像学研究中起着重要的作用。在这项工作中,我们提出了一种基于表面叶理理论的脑表面形态分析新方法。给定具有自动提取的地标曲线的大脑皮质表面,我们首先在表面上构造有限叶状结构。用户提供了一组允许的曲线和每个回路的高度参数。允许的曲线将表面切割成一组裤子。然后构造一个裤子分解图。通过计算曲面到裤子分解图的唯一谐波映射,得到Strebel微分。Strebel微分的临界轨迹将曲面分解为拓扑圆柱体。在将这些拓扑圆柱体与标准圆柱体共形映射后,标准圆柱体的参数(高度、周长)是原始皮质表面的固有几何特征,因此可以用于形态计量学分析。在这项工作中,我们提出了一组新的表面特征。据我们所知,这是第一个利用表面叶理理论进行脑形态分析的工作。我们计算的特征是内在的和信息丰富的。该方法具有严密、几何化、自动化等特点。阿尔茨海默病患者与健康对照者脑皮层表面分类的实验结果证明了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Surface Foliation Based Brain Morphometry Analysis.

Brain morphometry plays a fundamental role in neuroimaging research. In this work, we propose a novel method for brain surface morphometry analysis based on surface foliation theory. Given brain cortical surfaces with automatically extracted landmark curves, we first construct finite foliations on surfaces. A set of admissible curves and a height parameter for each loop are provided by users. The admissible curves cut the surface into a set of pairs of pants. A pants decomposition graph is then constructed. Strebel differential is obtained by computing a unique harmonic map from surface to pants decomposition graph. The critical trajectories of Strebel differential decompose the surface into topological cylinders. After conformally mapping those topological cylinders to standard cylinders, parameters of standard cylinders (height, circumference) are intrinsic geometric features of the original cortical surfaces and thus can be used for morphometry analysis purpose. In this work, we propose a set of novel surface features. To the best of our knowledge, this is the first work to make use of surface foliation theory for brain morphometry analysis. The features we computed are intrinsic and informative. The proposed method is rigorous, geometric, and automatic. Experimental results on classifying brain cortical surfaces between patients with Alzheimer's disease and healthy control subjects demonstrate the efficiency and efficacy of our method.

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Multimodal Brain Image Analysis and Mathematical Foundations of Computational Anatomy: 4th International Workshop, MBIA 2019, and 7th International Workshop, MFCA 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings BrainPainter: A software for the visualisation of brain structures, biomarkers and associated pathological processes. Surface Foliation Based Brain Morphometry Analysis.
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