论文标题
形态学人群分析的最佳传输特征
Optimal Transport Features for Morphometric Population Analysis
论文作者
论文摘要
脑病理通常表现为组织的部分或完全丧失。许多神经影像学研究的目的是捕获有关感兴趣的临床变量（例如疾病进展）的组织的位置和数量。形态分析方法捕获了与临床变量相关的组织分布或其他关注量的局部差异。我们建议通过基于不平衡的最佳传输的附加特征提取步骤来增强形态分析。最佳运输特征提取步骤增加了导致空间分散组织损失的病理的统计能力，从而最大程度地减少了由于空间不对对准或大脑拓扑的差异而对变化的敏感性，并将由于组织位置而导致的变化导致的变化而分开。我们证明了在阿尔茨海默氏病的OASIS-1研究的体积形态学分析的背景下，提出的最佳运输特征提取步骤。结果表明，所提出的方法可以识别组织的变化和差异，而差异是无法测量的。
Brain pathologies often manifest as partial or complete loss of tissue. The goal of many neuroimaging studies is to capture the location and amount of tissue changes with respect to a clinical variable of interest, such as disease progression. Morphometric analysis approaches capture local differences in the distribution of tissue or other quantities of interest in relation to a clinical variable. We propose to augment morphometric analysis with an additional feature extraction step based on unbalanced optimal transport. The optimal transport feature extraction step increases statistical power for pathologies that cause spatially dispersed tissue loss, minimizes sensitivity to shifts due to spatial misalignment or differences in brain topology, and separates changes due to volume differences from changes due to tissue location. We demonstrate the proposed optimal transport feature extraction step in the context of a volumetric morphometric analysis of the OASIS-1 study for Alzheimer's disease. The results demonstrate that the proposed approach can identify tissue changes and differences that are not otherwise measurable.
