University of Utah
Title: Computational Anatomy: Simple Statistics on Interesting Spaces for Developing Imaging Biomarkers Analysis
Date: Friday, February 16, 2024
Place and Time: Love 101, 3:05-3:55 pm
Abstract. A primary goal of Computational Anatomy is the statistical analysis of anatomical variability. Large deformation diffeomorphic transformations have been shown to accommodate geometric variability, but performing statistics on diffeomorphic transformations remains challenging. I will start with defining the "Average Anatomy" and then extend this to the study of regression and co-variation of anatomical shape with independent variables. The motivation is to model the inherent relation between anatomical shape and clinical measures and evaluate its statistical significance. We use Partial Least Squares for the multivariate statistical analysis of the deformation momenta under the Large Deformation Diffeomorphic framework. The statistical methodology extracts pertinent directions regarding latent variables in the momenta and clinical response spaces. I will present the results of applying this framework to numerous imaging studies, including our most recent results on modeling the Human Connectome.