An Improved Three-Dimensional Dual-Path Brain Tumor Image Segmentation Network

In recent years, the application of deep learning in biomedical image processing has received widespread attention. Based on the basic theories of deep learning and medical applications, this paper proposes an improved three-dimensional dual-path brain tumor image segmentation network to improve the detection accuracy of brain tumors in nuclear magnetic resonance imaging sequences. The proposed algorithm is based on 3D-UNet. First, the improved dual-path network unit is used to form the encoder-decoder structure similar to UNct. While retaining the original features, the network unit can also generate new features in texture, shape, and edge of the brain tumor to improve the accuracy of network segmentation. Second, the multi-fiber structure is added to the dual-path network module, which reduces the amount of parameters while ensuring the accuracy of the segmentation. Finally, after the group convolution in each network module, the channel random mixing module is added to solve the problem of accuracy reduction caused by group convolution, and the weighted Tversky loss function is used to replace the Dice loss function to improve the segmentation accuracy of small targets. The average Dice_ET, Dice_WT, and Dice_TC of the proposed model arc better than 3D-ESPNet, DeepMedic, DMFNet, and other algorithms. The research results have certain practical significance and application prospects.