Gopinath, Karthik;
Hoopes, Andrew;
Alexander, Daniel C;
Arnold, Steven E;
Balbastre, Yael;
Casamitjana, Adrià;
Cheng, You;
... Iglesias, Juan Eugenio; + view all
(2024)
Synthetic data in generalizable, learning-based neuroimaging.
Imaging Neuroscience
10.1162/imag_a_00337.
(In press).
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Abstract
Synthetic data has emerged as an attractive option for developing machine learning methods in human neuroimaging, particularly in magnetic resonance imaging (MRI)— a modality where image contrast depends enormously on acquisition hardware and parameters. This retrospective paper reviews a family of recently proposed methods, based on synthetic data, for generalizable machine learning in brain MRI analysis. Central to this framework is the concept of domain randomization, which involves training neural networks on a vastly diverse array of synthetically generated images with random contrast properties. This technique has enabled robust, adaptable models that are capable of handling diverse MRI contrasts, resolutions, and pathologies, while working out-of-the-box, without retraining. We have successfully applied this method to tasks such as whole brain segmentation (SynthSeg), skull-stripping (SynthStrip), registration (SynthMorph, EasyReg), super-resolution and MR contrast transfer (SynthSR). Beyond these applications, the paper discusses other possible use cases and future work in our methodology. Neural networks trained with synthetic data enable the analysis of clinical MRI, including large retrospective datasets, while greatly alleviating (and sometimes eliminating) the need for substantial labeled datasets, and offer enormous potential as robust tools to address various research goals.
| Type: | Article |
|---|---|
| Title: | Synthetic data in generalizable, learning-based neuroimaging |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1162/imag_a_00337 |
| Publisher version: | http://dx.doi.org/10.1162/imag_a_00337 |
| Language: | English |
| Additional information: | © 2024 The Authors. Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license (https://creativecommons.org/licenses/by/4.0/legalcode). |
| Keywords: | SynthSeg, SynthStrip, SynthMorph, EasyReg, SynthSR |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS 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/10199394 |
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