  In recent years, the use of deep learning is becoming increasingly popular in
computer vision. However, the effective training of deep architectures usually
relies on huge sets of annotated data. This is critical in the medical field
where it is difficult and expensive to obtain annotated images. In this paper,
we use Generative Adversarial Networks (GANs) for synthesizing high quality
retinal images, along with the corresponding semantic label-maps, to be used
instead of real images during the training process. Differently from other
previous proposals, we suggest a two step approach: first, a progressively
growing GAN is trained to generate the semantic label-maps, which describe the
blood vessel structure (i.e. vasculature); second, an image-to-image
translation approach is used to obtain realistic retinal images from the
generated vasculature. By using only a handful of training samples, our
approach generates realistic high resolution images, that can be effectively
used to enlarge small available datasets. Comparable results have been obtained
employing the generated images in place of real data during training. The
practical viability of the proposed approach has been demonstrated by applying
it on two well established benchmark sets for retinal vessel segmentation, both
containing a very small number of training samples. Our method obtained better
performances with respect to state-of-the-art techniques.
