  One of the biggest issues facing the use of machine learning in medical
imaging is the lack of availability of large, labelled datasets. The annotation
of medical images is not only expensive and time consuming but also highly
dependent on the availability of expert observers. The limited amount of
training data can inhibit the performance of supervised machine learning
algorithms which often need very large quantities of data on which to train to
avoid overfitting. So far, much effort has been directed at extracting as much
information as possible from what data is available. Generative Adversarial
Networks (GANs) offer a novel way to unlock additional information from a
dataset by generating synthetic samples with the appearance of real images.
This paper demonstrates the feasibility of introducing GAN derived synthetic
data to the training datasets in two brain segmentation tasks, leading to
improvements in Dice Similarity Coefficient (DSC) of between 1 and 5 percentage
points under different conditions, with the strongest effects seen fewer than
ten training image stacks are available.
