Research on Brain Glioma Segmentation Algorithm

Due to the complexity of the imaging technology of medical imaging and the high heterogeneity of the surface of gliomas, image segmentation of human brain gliomas is one of the most challenging tasks in medical image analysis. This paper improves the UNet++ medical image segmentation network, in the down-sampling stage of the decoder, crosschannel fusion is carried out and deep supervision is introduced, at this time, the improved network can fuse coarsegrained semantics and fine-grained semantics at full scale. Experiments were performed on 335 images in the public BraTS brain tumor segmentation data set, using 2D and 3D comparative segmentation experiments to comprehensively evaluate the segmentation performance of the improved network, and compare the segmentation results with the results of UNet, UNet++, and UNet3 medical image segmentation networks. Among the four indicators of Dice Similarity Coefficient (DSC), 95% Hausdorff surface distance(HSD95), Sensitivity, and Positive Predictive Value (PPV), 2D contrast segmentation is achieved the mean values of the indicators are: 83.70%, 1.7, 88.40%, 84.96%; the mean values of the 3D contrast segmentation experiment are: 90.79%, 0.242, 91.23%, 91.06%. Compared with the segmentation result indicators of the other three networks, in the 2D comparison experiment, DSC increased by 1.82% on average, HSD95 decreased by 0.35 on average, Sensitivity increased by 2.13% on average, and PPV increased by 0.80% on average; in the 3D comparison experiment, DSC increased by 2.78% on average, HSD95 decreased by 0.076 on average, Sensitivity increased by 3.81% on average, and PPV increased by 0.68% on average. The experiments show that the improved algorithm makes the segmentation result of glioma and the gold standard overlap more in the region, and can better complete the segmentation of glioma. In clinical applications, it can help neurosurgeons to effectively separate brain tumors and tissues around the human brain, and achieve rapid computer diagnosis and treatment.