Multimodal Brain Tumor MR Image Segmentation Network fused with Attention Mechanism

For the traditional multi-modal MR image segmentation methods, the correlation between different modal features, the global and local features were not fully considered, which leads to the reduction of segmentation accuracy. To solve such problem, a multi-modal brain tumor MR image segmentation method was proposed based on the attention mechanism. First, a triple attention module was proposed to enhance the correlation between the modal features and to accurately judge the position and boundary information of the region of interest; Then, the spatial and channel attention module was designed to capture the global and local features of the space and channel, and enhance the learning ability of tumor tissue structure information. The experimental results on the public datasets brats18 and brats19 show that the method achieves 90.62%, 87.89%, 90.08% and 2.2583 in the Dice coefficient, precision, sensitivity and Hausdorff distance when segmenting the whole tumor, respectively, which are better than similar methods in comparison.