Pixel-based classification method for corpus callosum segmentation on diffusion-MRI

Abstract

The Corpus Callosum (CC) is an important brain structure and its volume and variations in shape are correlated with diseases like Alzheimer, schizophrenia, dyslexia, epilepsy and multiple sclerosis. CC segmentation is a necessary step in both clinical and research studies. CC is commonly studied using structural Magnetic Resonance Imaging (MRI); evaluation and segmentation on Diffusion-MRI is important because there is relevant fiber and tissue information presented on these images, although it is challenging and rarely considered. In this work, a pixel-based classifier on Diffusion-MRI (directly in Diffusion-Weighted Imaging) using a Support Vector Machine is proposed for CC segmentation. A subsampling technique, based on K-means clustering, is used to treat the intrinsically unbalanced pixel classification problem. STAPLE algorithm is used to estimate both a silver-standard and a quantitative analysis through sensitivity, specificity and the Dice coefficient metrics. Our method reached a median value of 88% in Dice coefficient, had no initialization or parameters to be set and it was compared with two state-of-the-art approaches, showing higher CC detection rate.