Bayesian Spatial Binary Regression for Label Fusion in Structural Neuroimaging

Abstract

Alzheimer’s disease is a neurodegenerative condition that accelerates cognitive decline relative to normal aging. It is of critical scientific importance to gain a better understanding of early disease mechanisms in the brain to facilitate effective, targeted therapies. 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. Automatic hippocampal segmentation often involves mapping a previously manually segmented image to a new brain image and propagating the labels to obtain an estimate of where each hippocampus is located in the new image. A more 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. The fully Bayesian approach also produces meaningful uncertainty measures about hippocampal volumes, information which can be leveraged to detect significant, scientifically meaningful differences between healthy and diseased populations, improving the potential for early detection and tracking of the disease. Supplementary materials for this article are available online.

Keywords:

• Alzheimer’s disease
• Chromatic Gibbs sampling
• Conditionally autoregressive model
• Hippocampus segmentation
• Regions of interest

Supplementary materials

The online Supplementary Material contains additional details of the MCMC algorithms used, as well as additional figures that are helpful but not essential to the reader.

Acknowledgments

The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding agencies. The authors thank the Editor, an Associate Editor, anonymous referees, and the clinical neuroimaging research group in the Department of Biostatistics at Johns Hopkins University, particularly John Muschelli for his help with the image registrations.