An attention based residual U-Net with swin transformer for brain MRI segmentation

Abstract

Brain Tumors are a life-threatening cancer type. Due to the varied types and aggressive nature of these tumors, medical diagnostics faces significant challenges. Effective diagnosis and treatment planning depends on identifying the brain tumor areas from MRI images accurately. Traditional methods tend to use manual segmentation, which is costly, time consuming and prone to errors. Automated segmentation using deep learning approaches has shown potential in detecting tumor region. However, the complexity of the tumor areas which contain various shapes, sizes, fuzzy boundaries, makes this process difficult. Therefore, a robust automated segmentation method in brain tumor segmentation is required. In our paper, we present a hybrid model, 3-Dimension (3D) ResAttU-Net-Swin, which combines residual U-Net, attention mechanism and swin transformer. Residual blocks are introduced in the U-Net structure as encoder and decoder to avoid vanishing gradient problems and improve feature recovery. Attention-based skip connections are used to enhance the feature information transition between the encoder and decoder. The swin transformer obtains broad-scale features from the image data. The proposed hybrid model was evaluated on both the BraTS 2020 and BraTS 2019 datasets. It achieved an average Dice Similarity Coefficients (DSC) of 88.27 % and average Intersection over Union (IoU) of 79.93 % on BraTS 2020. On BraTS 2019, the model achieved an average DSC of 89.20 % and average IoU of 81.40 %. The model obtains higher DSC than the existing methods. The experiment result shows that the proposed methodology, 3D ResAttU-Net-Swin can be a potential for brain tumor segmentation in clinical settings.