Glioma Segmentation Strategies in 5G Teleradiology

As an essential part of the telemedicine system, teleradiology not only realizes mutual recognition of image diagnosis among medical institutions, but also ensures the quality of medical image diagnosis. In this paper, an edge computing (EC) driven 5G teleradiology framework is proposed to segment glioma accurately. In our framework, through the high-speed transmission and sharing mechanism of medical image data under 5G condition, the high-definition magnetic resonance imaging (MRI) images data of glioma patients are delivered to the edge server for data augmentation and training. Three typical data augmentation methods, i.e., traditional geometric transformation, DCGAN, and CycleGAN, are used to study the impact of the proportions of synthetic images generated by the above data augmentation methods to original MRI images on the performance of glioma segmentation. It is observed that training with different proportions leads to different segmentation results. Based on this observation, an efficient data augmentation strategy is established, which exploits the optimal proportions of synthetic and original data to yield the best glioma segmentation results in terms of sensitivity, specificity, accuracy, and Dice score. Experiments on the BraTS dataset show that the proposed data augmentation strategy improves the average Dice score of Dense U-net compared to the conventional data augmentation method. With the aid of our EC driven 5G teleradiology framework, accurate glioma segmentation results can be acquired rapidly at the terminal equipment to facilitate the online diagnosis.