Realistic generation of diffusion-weighted magnetic resonance brain images with deep generative models
Alejandro Ungría Hirte, Moritz Platscher, T. A. Joyce, Jeremy J. Heit, Eric Tranvinh, Christian Federau
Abstract
We study two state of the art deep generative networks, the Introspective Variational Autoencoder and the Style-Based Generative Adversarial Network, for the generation of new diffusion-weighted magnetic resonance images. We show that high quality, diverse and realistic-looking images, as evaluated by external neuroradiologists blinded to the whole study, can be synthesized using these deep generative models. We evaluate diverse metrics with respect to quality and diversity of the generated synthetic brain images. These findings show that generative models could qualify as a method for data augmentation in the medical field, where access to large image database is in many aspects restricted.