  Training segmentation networks requires large annotated datasets, which in
medical imaging can be hard to obtain. Despite this fact, data augmentation has
in our opinion not been fully explored for brain tumor segmentation. In this
project we apply different types of data augmentation (flipping, rotation,
scaling, brightness adjustment, elastic deformation) when training a standard
3D U-Net, and demonstrate that augmentation significantly improves the
network's performance in many cases. Our conclusion is that brightness
augmentation and elastic deformation work best, and that combinations of
different augmentation techniques do not provide further improvement compared
to only using one augmentation technique. Our code is available at
https://github.com/mdciri/3D-augmentation-techniques.
