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Recently, generative adversarial networks exhibited excellent performances insemi-supervised image analysis scenarios. In this paper, we go even further byproposing a fully unsupervised approach for segmentation applications withprior knowledge of the objects' shapes. We propose and investigate differentstrategies to generate simulated label data and perform image-to-imagetranslation between the image and the label domain using an adversarial model.Specifically, we assess the impact of the annotation model's accuracy as wellas the effect of simulating additional low-level image features. Forexperimental evaluation, we consider the segmentation of the glomeruli, anapplication scenario from renal pathology. Experiments provide proof of conceptand also confirm that the strategy for creating the simulated label data is ofparticular relevance considering the stability of GAN trainings.

Unsupervisedly Training GANs for Segmenting Digital Pathology with Automatically Generated Annotations