  Automatic segmentation of glioma and its subregions is of great significance
for diagnosis, treatment and monitoring of disease. In this paper, an
augmentation method, called TensorMixup, was proposed and applied to the three
dimensional U-Net architecture for brain tumor segmentation. The main ideas
included that first, two image patches with size of 128 in three dimensions
were selected according to glioma information of ground truth labels from the
magnetic resonance imaging data of any two patients with the same modality.
Next, a tensor in which all elements were independently sampled from Beta
distribution was used to mix the image patches. Then the tensor was mapped to a
matrix which was used to mix the one-hot encoded labels of the above image
patches. 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 to
segment glioma. The experimental results show that the mean accuracy of Dice
scores are 91.32%, 85.67%, and 82.20% respectively on the whole tumor, tumor
core, and enhancing tumor segmentation, which proves that the proposed
TensorMixup is feasible and effective for brain tumor segmentation.
