  Automatic brain tumor segmentation from Magnetic Resonance Imaging (MRI) data
plays an important role in assessing tumor response to therapy and personalized
treatment stratification.Manual segmentation is tedious and
subjective.Deep-learning-based algorithms for brain tumor segmentation have the
potential to provide objective and fast tumor segmentation.However, the
training of such algorithms requires large datasets which are not always
available. Data augmentation techniques may reduce the need for large
datasets.However current approaches are mostly parametric and may result in
suboptimal performance.We introduce two non-parametric methods of data
augmentation for brain tumor segmentation: the mixed structure regularization
(MSR) and shuffle pixels noise (SPN).We evaluated the added value of the MSR
and SPN augmentation on the brain tumor segmentation (BraTS) 2018 challenge
dataset with the encoder-decoder nnU-Net architecture as the segmentation
algorithm.Both MSR and SPN improve the nnU-Net segmentation accuracy compared
to parametric Gaussian noise augmentation.Mean dice score increased from 80% to
82% and p-values=0.0022, 0.0028 when comparing MSR to non-parametric
augmentation for the tumor core and whole tumor experiments respectively.The
proposed MSR and SPN augmentations have the potential to improve
neural-networks performance in other tasks as well.
