Comparative Study of Deep Learning Models for Segmentation of Corpus Callosum

A vital part of the Brain, which is responsible for the transmission of neural messages between the two hemispheres in the Brain is the Corpus Callosum. Many of the neurodegenerative diseases are related to the morphological properties of the Corpus Callosum. Therefore, its study and analysis become an essential part for the detection of such diseases. Examination of the Magnetic Resonance Images (MRI) through the mid-sagittal plane portrays their structure in the most distinguished manner. This paper carries out a comparative study of three deep learning models such as CE-Net, UNet++ & MultiResUNet for the segmentation of Corpus Callosum in the Brain MRI images using the dataset acquired from open source ABIDE platform. CE-Net gave the best dice similarity coefficient score of 0.9311, among all the three Deep Learning models. Thus, the CE-Net segmentation model can be further used for the classification of neurological disorders.