ArticleImaging neuroscience (Cambridge, Mass.)2024
Synthetic data in generalizable, learning-based neuroimaging.
Article in Imaging neuroscience (Cambridge, Mass.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- MindGrab: A spectrally-motivated architecture for accessible deep learning in neuroimaging.NeuroImage · 2026Article
- Automated detection of cerebral microbleeds on ex-vivo MRI scans of community-based older adults.NeuroImage · 2026Article
- Generative AI for spatial tumor growth on MRI: a proof-of-principle study in pediatric diffuse midline glioma.BMC medicine · 2026Article
- From Low Field to High Value: Robust Cortical Mapping From Low-Field MRI.Human brain mapping · 2026Article
- How meaningfulness and initial learning degree influence associative storage and retrieval processes: A multinomial processing tree and power function analysis.Psychonomic bulletin & review · 2026Article
- VesSynth: Tubes Are All You Need for Robust Cross-Scale Cross-Modal 3D Vessel Segmentation.bioRxiv : the preprint server for biology · 2026Article
- Improving Neuropathological Reconstruction Fidelity via AI Slice Imputation.Research square · 2026Article
- Brain-age in ultra-low-field MRI: How well does it work?Imaging neuroscience (Cambridge, Mass.) · 2026Article
- Deep infant brain segmentation from multi-contrast MRI.Conference record. Asilomar Conference on Signals, Systems & Computers · 2025Article
- Domain-Randomized Deep Learning for Neuroimage Analysis: Selecting Training Strategies, Navigating Challenges, and Maximizing Benefits.IEEE signal processing magazine · 2025Article
- Evaluating analytic strategies to obtain high-resolution, vertex-level measures of cortical neuroanatomy in children in low- and middle-income countries.Communications biology · 2025Article
- MultiMorph: On-demand Atlas Construction.Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition · 2025Article
- Strategies to Improve the Robustness and Generalizability of Deep Learning Segmentation and Classification in Neuroimaging.BioMedInformatics · 2025Article
- Review
- SNAKE: A modular realistic fMRI data simulator from the space-time domain to k-space and back.Imaging neuroscience (Cambridge, Mass.) · 2025Article
Corrections and comments
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Authors and funding
27 authors.
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Abstract
Synthetic data have 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.