Human anatomy, morphology, and associated diseases can be studied using
medical imaging data. However, access to medical imaging data is restricted by
governance and privacy concerns, data ownership, and the cost of acquisition,
thus limiting our ability to understand the human body. A possible solution to
this issue is the creation of a model able to learn and then generate synthetic
images of the human body conditioned on specific characteristics of relevance
(e.g., age, sex, and disease status). Deep generative models, in the form of
neural networks, have been recently used to create synthetic 2D images of
natural scenes. Still, the ability to produce high-resolution 3D volumetric
imaging data with correct anatomical morphology has been hampered by data
scarcity and algorithmic and computational limitations. This work proposes a
generative model that can be scaled to produce anatomically correct,
high-resolution, and realistic images of the human brain, with the necessary
quality to allow further downstream analyses. The ability to generate a
potentially unlimited amount of data not only enables large-scale studies of
human anatomy and pathology without jeopardizing patient privacy, but also
significantly advances research in the field of anomaly detection, modality
synthesis, learning under limited data, and fair and ethical AI. Code and
trained models are available at: https://github.com/AmigoLab/SynthAnatomy.Comment: 13 pages, 3 figures, 2 tables, accepted at SASHIMI MICCAI 202