Transformer-based architectures in MRI brain tumor segmentation: A review.

Area of Science:

• Deep Learning
• Computer Vision
• Medical Image Analysis

Background:

• Transformers are increasingly used in deep learning, with Vision Transformer (ViT) showing potential for automatic glioma MRI segmentation.
• ViT's receptive field aids in focusing on tumors and surrounding tissues, driving the development of variant models for medical image segmentation.

Purpose of the Study:

• To analyze Transformer variant algorithms for glioma MRI segmentation.
• To provide researchers with a reference and method comparisons for Transformer applications in this field.

Main Methods:

• Review of Transformer variant algorithms, focusing on model architecture design.
• Analysis of efficient self-attention mechanisms for feature capture.
• Examination of patch acquisition strategies and their influence on Vision Transformer efficacy.

Main Results:

• Transformer architectures, including Swin Transformer, offer structural optimizations for enhanced segmentation performance.
• The integration of Transformer and U-Net architectures is a predominant strategy in medical image segmentation.
• Effective self-attention mechanisms and optimized patch strategies significantly impact Vision Transformer performance.

Conclusions:

• Transformer variants offer significant promise for advancing automatic glioma MRI segmentation.
• Understanding architectural design, self-attention, and patch strategies is crucial for optimizing Transformer models in medical imaging.
• This review provides valuable insights and comparisons for researchers in the field of Transformer-based glioma MRI segmentation.