Tudosiu, Petru-Daniel;
Pinaya, Walter Hugo Lopez;
Graham, Mark S;
Borges, Pedro;
Fernandez, Virginia;
Yang, Dai;
Appleyard, Jeremy;
... Cardoso, Jorge; + view all
(2022)
Morphology-Preserving Autoregressive 3D Generative Modelling of the Brain.
In: Zhao, C and Svoboda, D and Wolterink, JM and Escobar, M, (eds.)
Simulation and Synthesis in Medical Imaging. SASHIMI 2022.
(pp. pp. 66-78).
Springer, Cham
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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.
| Type: | Proceedings paper |
|---|---|
| Title: | Morphology-Preserving Autoregressive 3D Generative Modelling of the Brain |
| Event: | 7th International Workshop on Simulation and Synthesis in Medical Imaging (SASHIMI) |
| Location: | Singapore, SINGAPORE |
| Dates: | 18 Sep 2022 |
| ISBN-13: | 978-3-031-16979-3 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/978-3-031-16980-9_7 |
| Publisher version: | https://doi.org/10.1007/978-3-031-16980-9_7 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Computer Science, Computer Science, Artificial Intelligence, Generative modelling, Life Sciences & Biomedicine, Mathematics, Mathematics, Applied, Neuroimaging, Neuromorphology, Physical Sciences, Radiology, Nuclear Medicine & Medical Imaging, Science & Technology, Technology, Transformers, VQ-VAE |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Brain Repair and Rehabilitation UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10159915 |
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