  Recently, generative adversarial networks exhibited excellent performances in
semi-supervised image analysis scenarios. In this paper, we go even further by
proposing a fully unsupervised approach for segmentation applications with
prior knowledge of the objects' shapes. We propose and investigate different
strategies to generate simulated label data and perform image-to-image
translation between the image and the label domain using an adversarial model.
Specifically, we assess the impact of the annotation model's accuracy as well
as the effect of simulating additional low-level image features. For
experimental evaluation, we consider the segmentation of the glomeruli, an
application scenario from renal pathology. Experiments provide proof of concept
and also confirm that the strategy for creating the simulated label data is of
particular relevance considering the stability of GAN trainings.
