@Article{cmes.2025.069396,
AUTHOR = {Khalil Ibrahim Lairedj, Zouaoui Chama, Amina Bagdaoui, Samia Larguech, Younes Menni, Nidhal Becheikh, Lioua Kolsi, Badr M. Alshammari},
TITLE = {Advanced Brain Tumor Segmentation in Magnetic Resonance Imaging via 3D U-Net and Generalized Gaussian Mixture Model-Based Preprocessing},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {144},
YEAR = {2025},
NUMBER = {2},
PAGES = {2419--2443},
URL = {http://www.techscience.com/CMES/v144n2/63739},
ISSN = {1526-1506},
ABSTRACT = {Brain tumor segmentation from Magnetic Resonance Imaging (MRI) supports neurologists and radiologists in analyzing tumors and developing personalized treatment plans, making it a crucial yet challenging task. Supervised models such as 3D U-Net perform well in this domain, but their accuracy significantly improves with appropriate preprocessing. This paper demonstrates the effectiveness of preprocessing in brain tumor segmentation by applying a pre-segmentation step based on the Generalized Gaussian Mixture Model (GGMM) to T1 contrast-enhanced MRI scans from the BraTS 2020 dataset. The Expectation-Maximization (EM) algorithm is employed to estimate parameters for four tissue classes, generating a new pre-segmented channel that enhances the training and performance of the 3D U-Net model. The proposed GGMM + 3D U-Net framework achieved a Dice coefficient of 0.88 for whole tumor segmentation, outperforming both the standard multiscale 3D U-Net (0.84) and MM U-Net (0.85). It also delivered higher Intersection over Union (IoU) scores compared to models trained without preprocessing or with simpler GMM-based segmentation. These results, supported by qualitative visualizations, suggest that GGMM-based preprocessing should be integrated into brain tumor segmentation pipelines to optimize performance.},
DOI = {10.32604/cmes.2025.069396}
}