Neurodisorders pose a considerable burden to global health, frequently requiring early treatment and diagnosis to avoid irreversible cognitive and motor impairments. Segmentation of brain tissue is an essential task in neurodiagnostics due to its inability to accurately separate grey matter, white matter, and cerebrospinal fluid within imaging modalities like MRI and CT. This article reviews the progress of brain tissue segmentation techniques from manual and semi-automated approaches to sophisticated machine learning and deep learning algorithms. It discusses how these contemporary methodologies enhance segmentation quality, address difficult anatomical variation, and optimize diagnostic accuracy in diseases like Alzheimer's, multiple sclerosis, traumatic brain injury, and stroke. This research work compares supervised, unsupervised, and deep learning paradigms on the basis of their advantages, disadvantages, and potential use in clinical settings. In addition, it presents hybrid and ensemble methods that blend conventional and AI-based approaches to mitigate obstacles such as data heterogeneity, annotation limitations, and interpretability. This survey details the revolutionizing potential of machine learning in neuroimaging and ultimately seeks to facilitate early diagnosis, treatment planning, and individualized healthcare provision for neurological diseases.
Kumar et al. (Tue,) studied this question.
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