  Automated brain tissue segmentation into white matter (WM), gray matter (GM),
and cerebro-spinal fluid (CSF) from magnetic resonance images (MRI) is helpful
in the diagnosis of neuro-disorders such as epilepsy, Alzheimer's, multiple
sclerosis, etc. However, thin GM structures at the periphery of cortex and
smooth transitions on tissue boundaries such as between GM and WM, or WM and
CSF pose difficulty in building a reliable segmentation tool. This paper
proposes a Fully Convolutional Neural Network (FCN) tool, that is a hybrid of
two widely used deep learning segmentation architectures SegNet and U-Net, for
improved brain tissue segmentation. We propose a skip connection inspired from
U-Net, in the SegNet architetcure, to incorporate fine multiscale information
for better tissue boundary identification. We show that the proposed U-SegNet
architecture, improves segmentation performance, as measured by average dice
ratio, to 89.74% on the widely used IBSR dataset consisting of T-1 weighted MRI
volumes of 18 subjects.
