MEASegNet: 3D U-Net with Multiple Efficient Attention for Segmentation of Brain Tumor Images

Brain tumors are a type of disease that affects people’s health and have received extensive attention. Accurate segmentation of Magnetic Resonance Imaging (MRI) images for brain tumors is essential for effective treatment strategies. However, there is scope for enhancing the segmentation accuracy of established deep learning approaches, such as 3D U-Net. In pursuit of improved segmentation precision for brain tumor MRI images, we propose the MEASegNet, which incorporates multiple efficient attention mechanisms into the 3D U-Net architecture. The encoder employs Parallel Channel and Spatial Attention Block (PCSAB), the bottleneck layer leverages Channel Reduce Residual Atrous Spatial Pyramid Pooling (CRRASPP) attention, and the decoder layer incorporates Selective Large Receptive Field Block (SLRFB). Through the integration of various attention mechanisms, we enhance the capacity for detailed feature extraction, facilitate the interplay among distinct features, and ensure the retention of more comprehensive feature information. Consequently, this leads to an enhancement in the segmentation precision of 3D U-Net for brain tumor MRI images. In conclusion, our extensive experimentation on the BraTS2021 dataset yields Dice scores of 92.50%, 87.49%, and 84.16% for Whole Tumor (WT), Tumor Core (TC), and Enhancing Tumor (ET), respectively. These results indicate a marked improvement in segmentation accuracy over the conventional 3D U-Net.