SCAR U-Net: A 3D Spatial-Channel Attention ResU-Net for Brain Tumor Segmentation

Although surgical resection is the best option for treating gliomas, it might be difficult to minimize the harm done to healthy brain regions. As a result, segmenting brain tumors in medical image analysis has become essential. A number of segmentation investigations utilizing CNNs have lately demonstrated promising performance thanks to the advancement of image equipment and deep learning techniques. In this study, we propose the SCAR U-Net, an end-to-end 3D residual U-Net model incorporating attention mechanisms for the segmentation of brain tumors. The SCAR U-Net employs channel and spatial attention processes and has a 3D U-Net architecture with residual blocks. We evaluate the model on a subset of the BraTS 2021 dataset. And the model outperforms the baseline significantly by ET, TC, and WT in the test set. Finally, we use ablation tests to confirm the beneficial effects of the residual connections and the attention modules.