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Automatic segmentation of glioma and its subregions is of great significancefor diagnosis, treatment and monitoring of disease. In this paper, anaugmentation method, called TensorMixup, was proposed and applied to the threedimensional U-Net architecture for brain tumor segmentation. The main ideasincluded that first, two image patches with size of 128 in three dimensionswere selected according to glioma information of ground truth labels from themagnetic resonance imaging data of any two patients with the same modality.Next, a tensor in which all elements were independently sampled from Betadistribution was used to mix the image patches. Then the tensor was mapped to amatrix which was used to mix the one-hot encoded labels of the above imagepatches. Therefore, a new image and its one-hot encoded label were synthesized.Finally, the new data was used to train the model which could be used tosegment glioma. The experimental results show that the mean accuracy of Dicescores are 91.32%, 85.67%, and 82.20% respectively on the whole tumor, tumorcore, and enhancing tumor segmentation, which proves that the proposedTensorMixup is feasible and effective for brain tumor segmentation.

A Data Augmentation Method for Fully Automatic Brain Tumor Segmentation