CCsNeT: Automated Corpus Callosum segmentation using fully convolutional network based on U-Net

Abstract

BackgroundCorpus Callosum (CC) is the most prominent white matter bundle in the human brain that connects the left and right cerebral hemispheres. The present paper proposes a novel method for CC segmentation from 2D T1- weighted mid-sagittal brain MRI. The robust segmentation of CC in the mid-sagittal plane plays a vital role in the quantitative study of CC structural features related to various neurological disorders such as Autism, epilepsy, Alzheimer's disease, and more. MethodologyIn this perspective, the current work proposes a Fully Convolutional Network (FCN), a deep learning architecture-based U-Net model for automated CC segmentation from 2D brain MRI images referred to as CCsNeT. The architecture consists of a 35-layers deep, fully convolutional network with two paths, namely contracting and extracting, connected in a U-shape that automatically extracts spatial information. ResultsThis attempt uses the benchmark brain MRI database comprising ABIDE and OASIS for the experimental investigation. Compared to existing CC segmentation methodologies, the proposed CCsNeT presented improved results achieving Dice Coefficient = 96.74%, and Sensitivity = 97.01% with ABIDE dataset and were further validated against the variants of U-Net model U-Net++, MultiResU-Net, and CE-Net. Further, the performance of CCsNeT has been validated on OASIS and Real-Time Images dataset. ConclusionFinally, the proposed CCsNeT extracts important CC characteristics such as CC area (CCA) and total brain area (TBA) to categorize the considered 2D MRI slices into control and autism spectrum disorder (ASD) groups, thereby minimizing the inter-observer and intra-observer variability.