Artificial Intelligence-Based Approaches for Brain Tumor Segmentation in MRI: A Review

Area of Science:

• Medical Image Analysis
• Artificial Intelligence in Oncology
• Neuroimaging Techniques

Background:

• Manual brain tumor segmentation in MRI is labor-intensive and requires extensive expertise.
• Automated segmentation using AI is vital for early tumor detection, diagnosis, and treatment planning.
• Advancements in machine learning and deep learning have spurred AI-driven solutions for brain tumor segmentation.

Purpose of the Study:

• To review MRI techniques and popular approaches for brain tumor segmentation.
• To highlight recent advancements in automated brain tumor segmentation.
• To identify challenges and future research directions in the field.

Main Methods:

• Comprehensive literature review of over 200 scholarly publications.
• Analysis of artificial intelligence techniques, including convolutional neural networks (CNNs) and vision transformers (ViTs).
• Examination of network architecture design, segmentation in unbalanced datasets, and multi-modality approaches.

Main Results:

• CNN-based methods and hybrid approaches demonstrate exceptional performance in segmenting brain tumors from MRI data.
• AI techniques significantly improve the speed and accuracy of tumor identification, type, size, and location assessment.
• The study synthesizes key findings from recent research in automated brain tumor segmentation.

Conclusions:

• AI-driven brain tumor segmentation is a rapidly advancing field with significant clinical implications.
• CNNs and hybrid models are highly effective for MRI-based brain tumor segmentation.
• Further research is needed to address existing challenges and explore novel techniques for improved segmentation accuracy and efficiency.