Multi-atlas based segmentation of corpus callosum on MRIs of multiple sclerosis patients

Abstract

In this work, a supervised automatic multi-atlas based segmentation method for corpus callosum (CC) in magnetic resonance images (MRIs) of MS patients is presented. Due to atrophy, the shape of disease affected CC differs distinctively from healthy ones. Therefore, atlases are used that are built from the underlying dataset and do not originate from atlas datasets of healthy brains. The atlas construction is done by clustering the patient images into subgroups of similar images and building a mean image from each cluster. During this work, the optimal number of atlases and the best label fusion method are analyzed. The method is evaluated on 100 T1-weighted brain MRI images from MS patients. Accuracy is assessed by comparing the overlap of the segmentations from the developed method against manual segmentations obtained by a medical student.