NeuroSeg Decoding the Brain a Multimodal Intelligence Framework for MRI Brain Tissue Segmentation Methods Machines and Medicine
DOI:
https://doi.org/10.15662/IJEETR.2026.0803008Keywords:
MRI brain segmentation, white matter, gray matter, deep learning, U-Net, nnU-Net, transformer, fuzzy C-means, atlas-based methods, bias field correction, partial volume effect, Dice coefficient, federated learning, neuroimagingAbstract
Magnetic Resonance Imaging (MRI) brain segmentation is a cornerstone of neuroimaging analysis, enabling clinicians and researchers to precisely delineate white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) compartments. This comprehensive review spans classical intensity-based methods (thresholding, k-means, fuzzy C-means), probabilistic atlas-guided frameworks (Expectation-Maximization, Gaussian Mixture Models), deformable surface models (active contours, level sets), and contemporary deep learning architectures including 3D U-Net, nnU-Net, and Swin-UNet transformers. Essential preprocessing stages — bias field correction (N4ITK), skull stripping (BET, HD-BET), and multimodal image registration (ANTs SyN) — are examined with original synthesised MRI visualisations illustrating each stage. The partial volume effect (PVE) and its correction strategies are discussed in detail. Validation methodologies using BrainWeb phantoms and the IBSR real-data repository are described alongside Dice similarity and Jaccard index benchmarks. Emerging frontiers including federated learning for privacy-preserving multi-site studies, uncertainty quantification, self-supervised pretraining, and neuroimaging foundation models are critically reviewed. This article serves as a structured reference for clinical researchers, biomedical engineers, and AI practitioners engaged in brain MRI analysis.
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