Deep Learning-Based Techniques in Glioma Brain Tumor Segmentation Using Multi-Parametric Mri: A Review on Clinical Applications and Future Outlooks Publisher Pubmed



Ghadimi DJ¹ ; Vahdani AM² ; Karimi H³ ; Ebrahimi P⁴ ; Fathi M¹ ; Moodi F^{5, 6} ; Habibzadeh A⁷ ; Khodadadi Shoushtari F⁶ ; Valizadeh G⁶ ; Mobarak Salari H⁶ ; Saligheh Rad H^{6, 8} Show Affiliations
Source: Journal of Magnetic Resonance Imaging Published:2025

Abstract

This comprehensive review explores the role of deep learning (DL) in glioma segmentation using multiparametric magnetic resonance imaging (MRI) data. The study surveys advanced techniques such as multiparametric MRI for capturing the complex nature of gliomas. It delves into the integration of DL with MRI, focusing on convolutional neural networks (CNNs) and their remarkable capabilities in tumor segmentation. Clinical applications of DL-based segmentation are highlighted, including treatment planning, monitoring treatment response, and distinguishing between tumor progression and pseudo-progression. Furthermore, the review examines the evolution of DL-based segmentation studies, from early CNN models to recent advancements such as attention mechanisms and transformer models. Challenges in data quality, gradient vanishing, and model interpretability are discussed. The review concludes with insights into future research directions, emphasizing the importance of addressing tumor heterogeneity, integrating genomic data, and ensuring responsible deployment of DL-driven healthcare technologies. Evidence Level: N/A. Technical Efficacy: Stage 2. © 2024 International Society for Magnetic Resonance in Medicine.