Abstract:
The automated segmentation of Brain MRI is an important initial step in the application of
anomaly detection and pain localization due to neurological diseases. In this view, we aim to
segment the sub structures (Caudate, Putamen, Pallidus and Accumbens) in the Basal-Ganglia
region of the brain which is responsible for controlled movement and routine learning. In this
paper we explore the possibility of using a convolutional neural network (SegNet) with long
skip connections for BG Segmentation. Segnet allows us to upsample the lower resolution in-
put features and does so with max-pooling weights only, thus reducing the training parameters
significantly and long skip connections are used to skip features from the downsampling path
to the expanding path in order to recover spatial information lost. Additionally we introduce
the use of 3D patch along with their motion boundaries as concatenated channels in the input
to the network which help in improved boundary segmentation. We use a public dataset of 83
T-1 weighted structural of healthy and disease MRIs available at [7] and produce a train and
test split of 50 and 33 mixed (healthy and all diseases) samples of MRIs respectively to ensure
a robust network.
