  A key step in understanding the spatial organization of cells and tissues is
the ability to construct generative models that accurately reflect that
organization. In this paper, we focus on building generative models of electron
microscope (EM) images in which the positions of cell membranes and
mitochondria have been densely annotated, and propose a two-stage procedure
that produces realistic images using Generative Adversarial Networks (or GANs)
in a supervised way. In the first stage, we synthesize a label "image" given a
noise "image" as input, which then provides supervision for EM image synthesis
in the second stage. The full model naturally generates label-image pairs. We
show that accurate synthetic EM images are produced using assessment via (1)
shape features and global statistics, (2) segmentation accuracies, and (3) user
studies. We also demonstrate further improvements by enforcing a reconstruction
loss on intermediate synthetic labels and thus unifying the two stages into one
single end-to-end framework.
