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Abstract：
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.
摘要：
目前,脑肿瘤是非常有害和普遍的。深度学习技术,包括CNN，UNet,变压器,在脑肿瘤分割中应用多年,取得了一定的成功。然而,传统的CNN和UNet捕获的全球信息不足，和变压器不能提供足够的本地信息。将来自Transformer的全局信息与卷积的局部信息融合是改善脑肿瘤分割的重要一步。我们提出了群体归一化洗牌和增强型信道自注意网络(GETNet)，将纯变压器结构与基于VT-UNet的卷积运算相结合的网络，它考虑了全球和本地信息。该网络包括所提出的组归一化混洗块(GNS)和增强型信道自注意块(ECSA)。在VT编码器块之后和下采样块之前使用GNS以改进信息提取。将ECSA模块添加到瓶颈层,以有效地利用底层中的详细特征的特性。我们还对BraTS2021数据集进行了实验，以证明我们网络的性能。Dice系数（Dice）评分结果表明，整个肿瘤（WT）区域的值，肿瘤核心（TC），和增强肿瘤（ET）分别为91.77、86.03和83.64。结果表明，与十一个以上的基准测试相比，该模型实现了最先进的性能。
