Deep Learning-Based Techniques in Glioma Brain Tumor Segmentation Using Multi-Parametric MRI: A Review on Clinical...

Area of Science:

• Neuroimaging and Artificial Intelligence
• Oncology and Medical Diagnostics

Background:

• Gliomas are complex brain tumors requiring precise segmentation for effective management.
• Multiparametric magnetic resonance imaging (MRI) provides rich data for characterizing gliomas.
• Traditional segmentation methods face challenges with tumor heterogeneity and complexity.

Purpose of the Study:

• To review the role and advancements of deep learning (DL) in glioma segmentation using multiparametric MRI.
• To explore clinical applications and future research directions for DL-based glioma segmentation.

Main Methods:

• Survey of deep learning techniques, particularly convolutional neural networks (CNNs), applied to multiparametric MRI data.
• Analysis of DL model evolution, including attention mechanisms and transformer models.
• Discussion of challenges such as data quality, gradient vanishing, and model interpretability.

Main Results:

• Deep learning models, especially CNNs, demonstrate significant capabilities in glioma segmentation from MRI.
• DL-based segmentation aids in clinical applications like treatment planning and response monitoring.
• Advancements show promise in overcoming segmentation complexities and improving diagnostic accuracy.

Conclusions:

• Deep learning is a powerful tool for advancing glioma segmentation accuracy and clinical utility.
• Future research should focus on tumor heterogeneity, genomic data integration, and responsible AI deployment.
• Continued development is crucial for optimizing DL-driven healthcare technologies in neuro-oncology.