  Automatic segmentation of brain Magnetic Resonance Imaging (MRI) images is
one of the vital steps for quantitative analysis of brain for further
inspection. In this paper, NeuroNet has been adopted to segment the brain
tissues (white matter (WM), grey matter (GM) and cerebrospinal fluid (CSF))
which uses Residual Network (ResNet) in encoder and Fully Convolution Network
(FCN) in the decoder. To achieve the best performance, various hyper-parameters
have been tuned, while, network parameters (kernel and bias) were initialized
using the NeuroNet pre-trained model. Different pre-processing pipelines have
also been introduced to get a robust trained model. The model has been trained
and tested on IBSR18 data-set. To validate the research outcome, performance
was measured quantitatively using Dice Similarity Coefficient (DSC) and is
reported on average as 0.84 for CSF, 0.94 for GM, and 0.94 for WM. The outcome
of the research indicates that for the IBSR18 data-set, pre-processing and
proper tuning of hyper-parameters for NeuroNet model have improvement in DSC
for the brain tissue segmentation.
