  Neural network-based approaches can achieve high accuracy in various medical
image segmentation tasks. However, they generally require large labelled
datasets for supervised learning. Acquiring and manually labelling a large
medical dataset is expensive and sometimes impractical due to data sharing and
privacy issues. In this work, we propose an adversarial data augmentation
method for training neural networks for medical image segmentation. Instead of
generating pixel-wise adversarial attacks, our model generates plausible and
realistic signal corruptions, which models the intensity inhomogeneities caused
by a common type of artefacts in MR imaging: bias field. The proposed method
does not rely on generative networks, and can be used as a plug-in module for
general segmentation networks in both supervised and semi-supervised learning.
Using cardiac MR imaging we show that such an approach can improve the
generalization ability and robustness of models as well as provide significant
improvements in low-data scenarios.
