  Deep learning requires large datasets for training (convolutional) networks
with millions of parameters. In neuroimaging, there are few open datasets with
more than 100 subjects, which makes it difficult to, for example, train a
classifier to discriminate controls from diseased persons. Generative
adversarial networks (GANs) can be used to synthesize data, but virtually all
research is focused on 2D images. In medical imaging, and especially in
neuroimaging, most datasets are 3D or 4D. Here we therefore present preliminary
results showing that a 3D progressive growing GAN can be used to synthesize MR
brain volumes.
