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Singh, Mahender Kumar and Singh, KK (2021) A Review of Publicly Available Automatic Brain Segmentation Methodologies, Machine Learning Models, Recent Advancements, and Their Comparison. Annals of Neurosciences. pp. 1-12.

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The noninvasive study of the structure and functions of the brain using neuroimaging techniques is increasingly being used for its clinical and research perspective. The morphological and volumetric changes in several regions and structures of brains are associated with the prognosis of neurological disorders such as Alzheimer’s disease, epilepsy, schizophrenia, etc. and the early identification of such changes can have huge clinical significance. The accurate segmentation of three-dimensional brain magnetic resonance images into tissue types (i.e., grey matter, white matter, cerebrospinal fluid) and brain structures, thus, has huge importance as they can act as early biomarkers. The manual segmentation though considered the “gold standard” is time-consuming, subjective, and not suitable for bigger neuroimaging studies. Several automatic segmentation tools and algorithms have been developed over the years; the machine learning models particularly those using deep convolutional neural network (CNN) architecture are increasingly being applied to improve the accuracy of automatic methods.

Item Type: Article
Subjects: Neurodegenerative Disorders
Neuro-Oncological Disorders
Neurocognitive Processes
Neuronal Development and Regeneration
Informatics and Imaging
Genetics and Molecular Biology
Depositing User: Dr. D.D. Lal
Date Deposited: 03 Aug 2021 08:59
Last Modified: 25 Nov 2021 05:09
URI: http://nbrc.sciencecentral.in/id/eprint/735

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