RFTNet: Region–Attention Fusion Network Combined with Dual-Branch Vision Transformer for Multimodal Brain Tumor Image Segmentation

Abstract

Brain tumor image segmentation plays a significant auxiliary role in clinical diagnosis. Recently, deep learning has been introduced into multimodal segmentation tasks, which construct various Convolutional Neural Network (CNN) structures to achieve excellent performance. However, most CNN-based segmentation methods have poor capability for global feature extraction. Transformer is good at modeling long-distance dependencies, but it can cause local information loss and usually has a high computational complexity. In addition, it is difficult to fully exploit the brain tumor features of different modalities. To address these issues, in this paper, we propose a region–attention fusion (RAF) network that combines a dual-branch vision Transformer (DVT), called RFTNet. In RFTNet, the DVT is exploited to capture the delicate local information and global semantics separately by two branches. Meanwhile, a novel RAF is employed to effectively fuse the images of the different modalities. Finally, we design a new hybrid loss function, called region-mixed loss function (RML) to calculate the importance of each pixel and solve the problem of class imbalance. The experiments on BrasTS2018 and BraTS2020 datasets show that our method obtains a higher segmentation accuracy than other models. Furthermore, ablation experiments prove the effectiveness of each key component in RFTNet.