Recent deep learning-based brain tumor segmentation models using multi-modality magnetic resonance imaging: a...

Area of Science:

• Medical Imaging
• Artificial Intelligence in Healthcare
• Neuro-oncology

Background:

• Brain tumor segmentation is critical for treatment planning but presents challenges for radiologists.
• Artificial intelligence (AI), particularly deep learning (DL), offers advanced tools to assist in this diagnostic process.
• Multi-modal magnetic resonance imaging (MRI) is increasingly utilized for detailed brain tumor analysis.

Purpose of the Study:

• To survey recent deep learning (DL) models for brain tumor segmentation using multi-modal MRI.
• To analyze current trends, datasets, and evaluation metrics in the field.
• To identify challenges and suggest future research directions for improved diagnostic accuracy.

Main Methods:

• Review of multi-modal MRI modalities and their characteristics.
• Categorization and discussion of DL models based on architecture: Convolutional Neural Networks (CNNs), Vision Transformers, and hybrid models.
• Statistical analysis of recent publications, datasets, and segmentation evaluation metrics.

Main Results:

• Identified and categorized various DL architectures (CNN, Transformer, hybrid) for brain tumor segmentation.
• Provided statistical insights into recent research, common datasets, and performance metrics.
• Highlighted open challenges and potential future research avenues.

Conclusions:

• Deep learning models show significant promise for improving brain tumor segmentation accuracy using multi-modal MRI.
• Further research is needed to address current challenges and optimize AI tools for clinical application.
• Advancements in AI-assisted segmentation can lead to better patient outcomes and personalized cancer care.