3D Brain and Heart Volume Generative Models: A Survey
It organizes existing research for medical imaging researchers, but is incremental as a survey.
This paper surveys generative models for 3D brain and heart volumes, proposing a new taxonomy to cover tasks like synthesis, classification, segmentation, and registration, and provides background, examination, and future directions.
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.