  Medical image segmentation is routinely performed to isolate regions of
interest, such as organs and lesions. Currently, deep learning is the state of
the art for automatic segmentation, but is usually limited by the need for
supervised training with large datasets that have been manually segmented by
trained clinicians. The goal of semi-superised and unsupervised image
segmentation is to greatly reduce, or even eliminate, the need for training
data and therefore to minimze the burden on clinicians when training
segmentation models. To this end we introduce a novel network architecture for
capable of unsupervised and semi-supervised image segmentation called
TricycleGAN. This approach uses three generative models to learn translations
between medical images and segmentation maps using edge maps as an intermediate
step. Distinct from other approaches based on generative networks, TricycleGAN
relies on shape priors rather than colour and texture priors. As such, it is
particularly well-suited for several domains of medical imaging, such as
ultrasound imaging, where commonly used visual cues may be absent. We present
experiments with TricycleGAN on a clinical dataset of kidney ultrasound images
and the benchmark ISIC 2018 skin lesion dataset.
