Due to the complexity of medical imaging techniques and the high heterogeneity of glioma surfaces, image segmentation of human gliomas is one of the most challenging tasks in medical image analysis. Current methods based on convolutional neural networks concentrate on feature extraction while ignoring the correlation between local and global. In this paper, we propose a residual mix transformer fusion net, namely RMTF-Net, for brain tumor segmentation. In the feature encoder, a residual mix transformer encoder including a mix transformer and a residual convolutional neural network (RCNN) is proposed. The mix transformer gives an overlapping patch embedding mechanism to cope with the loss of patch boundary information. Moreover, a parallel fusion strategy based on RCNN is utilized to obtain local-global balanced information. In the feature decoder, a global feature integration (GFI) module is applied, which can enrich the context with the global attention feature. Extensive experiments on brain tumor segmentation from LGG, BraTS2019 and BraTS2020 demonstrated that our proposed RMTF-Net is superior to existing state-of-art methods in subjective visual performance and objective evaluation.
Keywords: brain tumor segmentation; convolutional neural network; mix transformer; overlapping patch embedding mechanism.