TransBTS: Multimodal Brain Tumor Segmentation Using Transformer
This addresses brain tumor segmentation from MRI scans for medical diagnosis, representing an incremental improvement by integrating Transformer into 3D CNNs.
The authors tackled 3D MRI brain tumor segmentation by proposing TransBTS, a network combining 3D CNN and Transformer for local and global feature modeling, achieving comparable or higher results than previous state-of-the-art methods on BraTS 2019 and 2020 datasets.
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. Extensive experimental results on both BraTS 2019 and 2020 datasets show that TransBTS achieves comparable or higher results than previous state-of-the-art 3D methods for brain tumor segmentation on 3D MRI scans. The source code is available at https://github.com/Wenxuan-1119/TransBTS