  Deep learning has shown excellent performance in analysing medical images.
However, datasets are difficult to obtain due privacy issues, standardization
problems, and lack of annotations. We address these problems by producing
realistic synthetic images using a combination of 3D technologies and
generative adversarial networks. We use zero annotations from medical
professionals in our pipeline. Our fully unsupervised method achieves promising
results on five real polyp segmentation datasets. As a part of this study we
release Synth-Colon, an entirely synthetic dataset that includes 20000
realistic colon images and additional details about depth and 3D geometry:
https://enric1994.github.io/synth-colon
