Deep neural networks for segmentation of basal ganglia sub-structures in brain MR images

Abstract

Automated segmentation of brain structure in magnetic resonance imaging (MRI) scans is an important first step in diagnosis of many neurological diseases. In this paper, we focus on segmentation of the constituent sub-structures of basal ganglia (BG) region of the brain that are responsible for controlling movement and routine learning. Low contrast voxels and undefined boundaries across sub-regions of BG pose a challenge for automated segmentation. We pose the segmentation as a voxel classification problem and propose a Deep Neural Network (DNN) based classifier for BG segmentation. The DNN is able to learn distinct regional features for voxel-wise classification of BG area into four sub-regions, namely, Caudate, Putamen, Pallidum, and Accumbens. We use a public dataset with a collection of 83 T-1 weighted uniform dimension structural MRI scans of healthy and diseased (Bipolar with and without Psychosis, Schizophrenia) subjects. In order to build a robust classifier, the proposed classifier has been trained on a mixed collection of healthy and diseased MRs. We report an accuracy of above 94% (as calculated using the dice coefficient) for all the four classes of healthy and diseased dataset.