Bayesian Spatial Binary Regression for Label Fusion in Structural Neuroimaging

• Activity Number: 494 - Advanced Developments in Methods and Algorithms for Modern Complex Imaging Data
• Type: Invited
• Date/Time: Thursday, August 11, 2022 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistical Computing
• Author(s): Andrew Brown* and Christopher McMahan and Russell Shinohara and Kristin Linn
• Companies: Clemson University and Clemson University and University of Pennsylvania and University of Pennsylvania
• Keywords: Alzheimer's disease; chromatic Gibbs sampling; conditionally autoregressive model; hippocampus segmentation; regions of interest

Abstract

Alzheimer's disease is a neurodegenerative condition that accelerates cognitive decline relative to normal aging. The volume of the hippocampus is often used in diagnosis and monitoring of the disease. Measuring this volume via neuroimaging is difficult since each hippocampus must either be manually identified or automatically delineated, a task referred to as segmentation. A recent approach to this problem is to propagate labels from multiple manually segmented atlases and combine the results using a process known as label fusion. To date, most label fusion algorithms employ voting procedures with voting weights assigned directly or estimated via optimization. We propose using a fully Bayesian spatial regression model for label fusion that facilitates direct incorporation of covariate information while making accessible the entire posterior distribution. Our results suggest that incorporating tissue classification (e.g, gray matter) into the label fusion procedure can greatly improve segmentation when relatively homogeneous, healthy brains are used as atlases for diseased brains.