  We develop and approach to unsupervised semantic medical image segmentation
that extends previous work with generative adversarial networks. We use
existing edge detection methods to construct simple edge diagrams, train a
generative model to convert them into synthetic medical images, and construct a
dataset of synthetic images with known segmentations using variations on
extracted edge diagrams. This synthetic dataset is then used to train a
supervised image segmentation model. We test our approach on a clinical dataset
of kidney ultrasound images and the benchmark ISIC 2018 skin lesion dataset. We
show that our unsupervised approach is more accurate than previous unsupervised
methods, and performs reasonably compared to supervised image segmentation
models. All code and trained models are available at
https://github.com/kiretd/Unsupervised-MIseg.
