MBANet: A 3D convolutional neural network with multi-branch attention for brain tumor segmentation from MRI images

Abstract

More than half of brain tumors are malignant tumors, so there is a need for fast and accurate segmentation of tumor regions in brain Magnetic Resonance Imaging (MRI) images. Traditional 2D brain tumor segmentation methods seriously ignore the spatial context features of brain tumor MRI images, so how to achieve accurate segmentation of brain tumor regions with multiple modalities is the main problem. The paper proposes a 3D convolutional neural network with 3D multi-branch attention - MBANet. First, the optimized shuffle unit is used to form the basic unit (BU) module of MBANet. In the BU module, the group convolution is used to perform convolution operation after the input channel is split, and the channel shuffle is used to scramble the convolutional channels after fusion. Then, MBANet uses a novel multi-branch 3D Shuffle Attention (SA) module as the attention layer in the encoder. The 3D SA module groups along the channel dimension and divides the feature maps into small features. For each small feature, the 3D SA module builds both channel attention and spatial attention while adopting the BU module. In addition, in order to recover the resolution of the upsampling semantic features better, a 3D SA module is also used in the skip connection of MBANet. Experiments on the BraTS 2018 and BraTS 2019 show that the dice of ET, WT and TC reach 80.18%, 89.80%, 85.47% and 78.21%, 89.79%, 83.04%, respectively. The excellent segmentation performance shows that MBANet is significantly improved compared with other state-of-the-art methods.