Brain tumor segmentation with deep learning: Current approaches and future perspectives.

Area of Science:

• Medical Imaging Analysis
• Artificial Intelligence in Healthcare
• Computational Neuroscience

Background:

• Accurate brain tumor segmentation from MRI is crucial for effective diagnosis and treatment planning.
• Segmentation can be challenging due to image noise and abnormalities.
• Existing methods vary in complexity and effectiveness.

Purpose of the Study:

• To systematically review automatic brain tumor segmentation techniques.
• To focus on the design of network architectures, particularly deep learning methods.
• To compare performance, efficiency, and robustness across various datasets.

Main Methods:

• Categorization of methods into unsupervised and supervised learning (machine learning and deep learning).
• In-depth review of deep learning approaches: CNN-based, U-Net-based, transfer learning-based, transformer-based, and hybrid methods.
• Analysis of multi-modal MRI imaging and its impact on segmentation accuracy.

Main Results:

• Deep learning, especially U-Net architectures, has significantly improved medical image segmentation.
• Iterative improvements in U-Net models have driven progress in brain tumor segmentation.
• Evaluation of performance metrics, efficiency, and robustness on datasets like BraTS.

Conclusions:

• Automated segmentation approaches show significant efficiency, validated on the BraTS dataset.
• Identified current challenges in computer-aided diagnostic systems.
• Highlighted key areas for future research and development in brain tumor segmentation.