TY  - EJOU
AU  - Lairedj, Khalil Ibrahim 
AU  - Chama, Zouaoui 
AU  - Bagdaoui, Amina 
AU  - Larguech, Samia 
AU  - Menni, Younes 
AU  - Becheikh, Nidhal 
AU  - Kolsi, Lioua 
AU  - Alshammari, Badr M. 

TI  - Advanced Brain Tumor Segmentation in Magnetic Resonance Imaging via 3D U-Net and Generalized Gaussian Mixture Model-Based Preprocessing
T2  - Computer Modeling in Engineering \& Sciences

PY  - 2025
VL  - 144
IS  - 2
SN  - 1526-1506

AB  - 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.
KW  - Magnetic resonance imaging (MRI); imaging technology; GGMM; EM algorithm; 3D U-Net; segmentation

DO  - 10.32604/cmes.2025.069396