Morphology-preserving Autoregressive 3D Generative Modelling of the Brain

TL;DR

This paper introduces a novel 3D generative model that produces high-resolution, anatomically accurate brain images, enabling large-scale, privacy-preserving studies of human anatomy and disease.

Contribution

It presents a morphology-preserving autoregressive model capable of generating realistic 3D brain images, addressing data scarcity and computational challenges in medical imaging.

Findings

Generates high-resolution, anatomically correct 3D brain images

Enables large-scale, privacy-preserving anatomical studies

Advances anomaly detection and modality synthesis

Abstract

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.