GETNet: Group Normalization Shuffle and Enhanced Channel Self-Attention Network Based on VT-UNet for Brain Tumor Segmentation

被引:2
作者
Guo, Bin [1 ,2 ]
Cao, Ning [1 ]
Zhang, Ruihao [2 ]
Yang, Peng [2 ]
机构
[1] Hohai Univ, Coll Informat Sci & Engn, Nanjing 210098, Peoples R China
[2] Xinjiang Agr Univ, Coll Comp & Informat Engn, Urumqi 830052, Peoples R China
基金
中国国家自然科学基金;
关键词
brain tumor segmentation; MRI; medical image; deep learning; Transformer; TRANSFORMER; NET;
D O I
10.3390/diagnostics14121257
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Currently, brain tumors are extremely harmful and prevalent. Deep learning technologies, including CNNs, UNet, and Transformer, have been applied in brain tumor segmentation for many years and have achieved some success. However, traditional CNNs and UNet capture insufficient global information, and Transformer cannot provide sufficient local information. Fusing the global information from Transformer with the local information of convolutions is an important step toward improving brain tumor segmentation. We propose the Group Normalization Shuffle and Enhanced Channel Self-Attention Network (GETNet), a network combining the pure Transformer structure with convolution operations based on VT-UNet, which considers both global and local information. The network includes the proposed group normalization shuffle block (GNS) and enhanced channel self-attention block (ECSA). The GNS is used after the VT Encoder Block and before the downsampling block to improve information extraction. An ECSA module is added to the bottleneck layer to utilize the characteristics of the detailed features in the bottom layer effectively. We also conducted experiments on the BraTS2021 dataset to demonstrate the performance of our network. The Dice coefficient (Dice) score results show that the values for the regions of the whole tumor (WT), tumor core (TC), and enhancing tumor (ET) were 91.77, 86.03, and 83.64, respectively. The results show that the proposed model achieves state-of-the-art performance compared with more than eleven benchmarks.
引用
收藏
页数:23
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