Deep Learning-Based Corpus Callosum Segmentation from Brain Images: A Review

Abstract

The largest white matter structure in the brain, the corpus callosum (CC), is involved in many disorders of the central nervous system. The extent and/or severity of neurodegenerative illnesses are correlated with its size. Though numerous approaches and procedures for CC fragmentation have been offered, and the role of CC has been scrutinized more and more over the past few centuries. Nevertheless, the segmentation accuracy of the current models is not very good. This research offers a thorough analysis of various segmentation methods for CC fragmentation. Additionally, it investigates the different deep learning models focused on CC segmentation obtained from brain magnetic resonance imaging. The results show that not all of the issues with the computational methods for segmenting CC on magnetic resonance imaging have been resolved.