Transformer-based architectures in MRI brain tumor segmentation: A review.
Authors
Affiliations (5)
Affiliations (5)
- School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Engineering Campus, Nibong Tebal, 14300, Penang, Malaysia; School of Electrical and Control Engineering, Ningxia Polytechnic University of Business and Technology, Yinchuan 750030, China. Electronic address: [email protected].
- Department of Neurosciences, School of Medical Sciences, Health Campus, Universiti Sains Malaysia, Kubang Kerian, Kelantan, 16150, Malaysia; Brain and Behaviour Cluster, School of Medical Sciences, Health Campus, Universiti Sains Malaysia, Kubang Kerian, Kelantan, 16150, Malaysia. Electronic address: [email protected].
- Centre for Global Sustainability Studies, Universiti Sains Malaysia, Main Campus, Minden, 11800 USM, Penang, Malaysia. Electronic address: [email protected].
- School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Engineering Campus, Nibong Tebal, 14300, Penang, Malaysia. Electronic address: [email protected].
- School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Engineering Campus, Nibong Tebal, 14300, Penang, Malaysia. Electronic address: [email protected].
Abstract
Transformers have been actively utilized in the deep learning field recently. Vision Transformer (ViT), as one of its important applications in the computer vision field, exhibits significant promise for automatic glioma MRI image segmentation. As the Transformer has a large efficient receptive field, it can focus on the tumor range as well as surrounding tissues and organs. Hence, many variant models derived from Transformer architecture have been developed for medical image segmentation. Effective model architectures can significantly enhance segmentation performance, and Swin Transformer is one typical structural optimization. Conversely, since both Transformer and U-Net have significant applications in medical image segmentation and enable seamless integration, their combination has become the predominant architectural design strategy. Furthermore, effective self-attention mechanisms have a strong ability to capture features, and the size and position of the patch also significantly influence the efficacy of ViT. In general, this paper analyzes the applications of the Transformer variant algorithms from model architecture design, efficient self-attention mechanism, and patch acquisition strategy. This paper concentrates on the applications of Transformers for glioma MRI segmentation, to give researchers in the area a reference and method comparisons.