Optimal Transport Features for Morphometric Population Analysis

TL;DR

This paper introduces an optimal transport-based feature extraction method to enhance morphometric analysis in neuroimaging, improving detection of spatially dispersed tissue loss and reducing sensitivity to misalignments.

Contribution

The novel contribution is the integration of unbalanced optimal transport into morphometric analysis, increasing statistical power and separating volume from location changes.

Findings

Enhanced detection of tissue changes in Alzheimer's disease.

Improved robustness to spatial misalignments.

Identification of otherwise unmeasurable tissue differences.

Abstract

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.