Enhanced UNet++ model for brain glioma MRI image segmentation

MRI technology can obtain the brain avatar of patients from multiple modes and angles, which greatly improves the diagnostic efficiency of brain tumor patients. However, the MRI data of brain tumor patients is usually not enough to train the deep learning based segmentation model for sufficient performance. This paper proposes a simple and effective method to augment the data to improve the image segmentation effect of UNet++ model on brain glioma. To enhance the illumination robust of MRI image, we bring in the max filter and min filter for image augmentation, and evaluate the segmentation effect of the UNet++ model using the original dataset and the augmented dataset on glioma, and evaluate the experimental results. We used the public datasets MICCAI_ BraTS_20 to evaluate, The WT PPV values of the two models were 0.9233 and 0.9427 respectively, and other evaluation indicators were also improved to varying degrees. In summary, it is concluded that our proposed simple augmentation method could effective augment the brain glioma MRI segmentation performance of UNet++ model on glioma, especially in the limited data.