Despite significant achievements of deep learning in medical image segmentation,there are challenges for brain tumor segmentation using deep learning,such as insufficient receptive field,excessive redundant information,and information loss.To address these issues,a novel brain tumor segmentation network model(EAU-Net)is proposed based on encoder-decoder structure.EAU-Net incorporates an effective receptive field expansion block and an attention fusion module to minimize the adverse effects caused by insufficient receptive field and excessive redundant information which often occurred in the current brain tumor segmentation network.Additionally,a bottleneck resampling module based on inverted residual structure is introduced to effectively avoid information loss during upsampling and downsampling,while deep convolutions are used to reduce computational complexity.Experimental results on the BraTS2020 dataset reveal that EAU-Net achieves the highest segmentation accuracy,demonstrating its feasibility and effectiveness for brain tumor segmentation.
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