DA-ResUNet: A Novel Method for Brain Tumor Segmentation Based on U-Net with Residual Block and CBAM

This paper focuses on a new method for brain tumor segmentation in Magnetic Resonance Imaging (MRI), using a modified residual block and CBAM for the U-Net network. To deepen the network, we replace the convolutional layer with a residual block with a CBAM module. We also insert the CBAM dual-attention module after skip connection and upsampling at each layer. It solves the problem that the low-level features contain a lot of redundant information because the skip connection connects the feature maps extracted by the encoder directly to the corresponding layer of the decoder. The performance is evaluated on the MRI dataset of Medical Image Computing and Computer Aided Intervention Society (MICCAI) 2018 Brain Tumor Segmentation Challenge. Numerical results are presented in the form of Specifity, Sensitivity, HD_95 and Dice coefficient (DICE) for GD-enhancing tumor (ET), tumor core (TC) and whole tumor (WT), respectively. We compare the proposed method with expert manual method and other state-of-art methods. Experiments show that RDAU-Net achieves state-of-the-art performance.