Fig. 1.

Conditional GAN structure. The generator is a U-Net that progressively down-samples / encodes and then up-samples / decodes an input by a series of convolutional layers, with additional skip-connections between each major layer. The generated, ’fake’ segmentation image is then fed together with the ground truth segmentation image into a discriminator network (PatchGAN (Isola et al., 2017)) that gives its prediction of whether the generated image is a ‘real’ representation of the ground truth image, or not. A detailed description of the network architecture can be found in the Appendix.