Brain tumor segmentation of 3D magnetic resonance (MR) images is of great significance for brain diagnosis. Although the U-Net and its variants have achieved outstanding performance in medical image segmentation, there still exist some challenges somewhat due to the fact that the CNN based models are powerful in extracting local features but are powerless in capturing global representations. To tackle this problem, we propose a 3D network structure based on the nnUNet, named TSEUnet. In this network, the transformer module is introduced in the encoder in a parallel interactive manner so that both local features and global contexts can be efficiently extracted. Moreover, SE-Attention is also incorporated in the decoder to enhance the meaningful information and improve the segmentation accuracy for brain tumor area. In addition, we propose a post-processing method to further improve the brain tumor segmentation. Experiments on the BRATS 2018 dataset show that our proposed TSEUnet achieves better performance on brain tumor segmentation as compared with the state-of-the-art methods.