  Transformer, which can benefit from global (long-range) information modeling
using self-attention mechanisms, has been successful in natural language
processing and 2D image classification recently. However, both local and global
features are crucial for dense prediction tasks, especially for 3D medical
image segmentation. In this paper, we for the first time exploit Transformer in
3D CNN for MRI Brain Tumor Segmentation and propose a novel network named
TransBTS based on the encoder-decoder structure. To capture the local 3D
context information, the encoder first utilizes 3D CNN to extract the
volumetric spatial feature maps. Meanwhile, the feature maps are reformed
elaborately for tokens that are fed into Transformer for global feature
modeling. The decoder leverages the features embedded by Transformer and
performs progressive upsampling to predict the detailed segmentation map.
Experimental results on the BraTS 2019 dataset show that TransBTS outperforms
state-of-the-art methods for brain tumor segmentation on 3D MRI scans. Code is
available at https://github.com/Wenxuan-1119/TransBTS
