  Generative models such as generative adversarial networks and autoencoders
have gained a great deal of attention in the medical field due to their
excellent data generation capability. This paper provides a comprehensive
survey of generative models for three-dimensional (3D) volumes, focusing on the
brain and heart. A new and elaborate taxonomy of unconditional and conditional
generative models is proposed to cover diverse medical tasks for the brain and
heart: unconditional synthesis, classification, conditional synthesis,
segmentation, denoising, detection, and registration. We provide relevant
background, examine each task and also suggest potential future directions. A
list of the latest publications will be updated on Github to keep up with the
rapid influx of papers at
https://github.com/csyanbin/3D-Medical-Generative-Survey.
