  Modern generative models, such as generative adversarial networks (GANs),
hold tremendous promise for several areas of medical imaging, such as
unconditional medical image synthesis, image restoration, reconstruction and
translation, and optimization of imaging systems. However, procedures for
establishing stochastic image models (SIMs) using GANs remain generic and do
not address specific issues relevant to medical imaging. In this work,
canonical SIMs that simulate realistic vessels in angiography images are
employed to evaluate procedures for establishing SIMs using GANs. The GAN-based
SIM is compared to the canonical SIM based on its ability to reproduce those
statistics that are meaningful to the particular medically realistic SIM
considered. It is shown that evaluating GANs using classical metrics and
medically relevant metrics may lead to different conclusions about the fidelity
of the trained GANs. This work highlights the need for the development of
objective metrics for evaluating GANs.
