Due to the high heterogeneity of brain tumors, automatic segmentation of brain DEODORANT tumors remains a challenging task.In this paper, we propose RDAU-Net by adding dilated feature pyramid blocks with 3D CBAM blocks and inserting 3D CBAM blocks after skip-connection layers.Moreover, a CBAM with channel attention and spatial attention facilitates the combination of more expressive feature information, thereby leading to more efficient extraction of contextual information from images of various Vitamins D-3 scales.The performance was evaluated on the Multimodal Brain Tumor Segmentation (BraTS) challenge data.Experimental results show that RDAU-Net achieves state-of-the-art performance.
The Dice coefficient for WT on the BraTS 2019 dataset exceeded the baseline value by 9.2%.