The segmentation of white matter, grey matter, and cerebrospinal fluid (CSF) plays a crucial role in predicting and diagnosing neuro disorders. This study investigates the impact of precise segmentation techniques on identifying biomarkers and structural changes associated with various neurological conditions, including Alzheimer's disease, multiple sclerosis, and schizophrenia. By leveraging advanced imaging technologies and machine learning algorithms, we aim to enhance the accuracy of neuro disorder predictions through detailed analysis of brain tissue composition. Our findings demonstrate that accurate segmentation of brain components improves early detection and differentiation of neuro disorders and provides insights into disease progression and potential therapeutic targets. This research underscores the significance of integrating multi-modal imaging data for a comprehensive understanding of neurodegenerative and psychiatric diseases.

Keywords: White matter, Grey matter, and cerebrospinal fluid (CSF), Alzheimer’s disease, multiple sclerosis, Segmentation.

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