(untitled) (Samarra Journal of Pure and Applied Science, 2026)
This study proposes an intelligent framework for automated brain tissue segmentation in MRI scans using U-Net-based convolutional neural networks, aiming to improve accuracy over existing methods. The methodology involves implementing both 2D and 3D U-Net architectures, trained and validated on public datasets (MRBrainS13, MRBrainS18, IBSR) with manual expert segmentations. Performance was compared against established tools (FSL, Dipy) using Dice Coefficient, Jaccard Index, AUC, and SSIM metrics. Results demonstrate the superiority of U-Net models, with Dice scores exceeding 0.9 for gray matter and showing high consistency, while 2D U-Net slightly outperformed the 3D variant in reducing false positives and negatives. The conclusion confirms that U-Net-based segmentation significantly enhances precision and reliability for distinguishing gray matter, white matter, and cerebrospinal fluid, offering a robust alternative to conventional techniques