Corpus callosum segmentation in MS studies using normal atlases and optimal hybridization of extrinsic and intrinsic image cues

Abstract

The corpus callosum (CC) is a key brain structure and change in its size and shape is a focal point in the study of neurodegenerative diseases like multiple sclerosis (MS). A number of automatic methods have been proposed for CC segmentation in magnetic resonance images (MRIs) that can be broadly classified as intensity-based and template-based. Imaging artifacts and signal changes due to pathology often cause errors in intensity-based methods. Template-based methods have been proposed to alleviate these problems. However, registration inaccuracies (local mismatch) can occur when the template image has large intensity and morphological differences from the scan to be segmented, such as when using publicly available normal templates for a diseased population. Accordingly, we propose a novel hybrid segmentation framework that performs optimal, spatially variant fusion of multi-atlas-based and intensity-based priors. Our novel coupled graph-labeling formulation effectively optimizes, on a per-voxel basis, the weights that govern the choice of priors so that intensity priors derived from the subject image are emphasized when spatial priors derived from the registered templates are deemed less trustworthy. This stands in contrast to existing hybrid methods that either ignore local registration errors or alternate between the optimization of fusion weights and segmentation results in an expectation maximization fashion. We evaluated our method using a public dataset and two large in-house MS datasets and found that it gave more accurate results than those achieved by existing methods for CC segmentation.