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Modern generative models, such as generative adversarial networks (GANs),hold tremendous promise for several areas of medical imaging, such asunconditional medical image synthesis, image restoration, reconstruction andtranslation, and optimization of imaging systems. However, procedures forestablishing stochastic image models (SIMs) using GANs remain generic and donot address specific issues relevant to medical imaging. In this work,canonical SIMs that simulate realistic vessels in angiography images areemployed to evaluate procedures for establishing SIMs using GANs. The GAN-basedSIM is compared to the canonical SIM based on its ability to reproduce thosestatistics that are meaningful to the particular medically realistic SIMconsidered. It is shown that evaluating GANs using classical metrics andmedically relevant metrics may lead to different conclusions about the fidelityof the trained GANs. This work highlights the need for the development ofobjective metrics for evaluating GANs.

Evaluating Procedures for Establishing Generative Adversarial Network-based Stochastic Image Models in Medical Imaging