ArticleScientific reports2023
A ready-to-use machine learning tool for symmetric multi-modality registration of brain MRI.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
40 citing papers in PubMed, 49 citations in OpenAlex.
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- Optimal Deep Brain Stimulation Locations for Gilles de la Tourette Syndrome.medRxiv : the preprint server for health sciences · 2026Article
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- Investigating the Utility of Explainable Artificial Intelligence for Neuroimaging-Based Dementia Diagnosis and Prognosis.Human brain mapping · 2026Article
- White matter resection and verbal memory deficits after temporal lobe epilepsy surgery.Brain communications · 2026Article
- MICAFlow: Fast and robust MRI preprocessing bridging research neuroimaging and clinical practice.Imaging neuroscience (Cambridge, Mass.) · 2026Article
- Increased susceptibility and volume reduction in deep brain nuclei in primary orthostatic tremor.BMJ neurology open · 2026Article
- AI-powered in silico twins: redefining precision medicine through simulation, personalization, and predictive healthcare.Saudi pharmaceutical journal : SPJ : the official publication of the Saudi Pharmaceutical Society · 2025Review
- Article
- A human brain network linked to restoration of consciousness after deep brain stimulation.Nature communications · 2025Article
- Domain-Randomized Deep Learning for Neuroimage Analysis: Selecting Training Strategies, Navigating Challenges, and Maximizing Benefits.IEEE signal processing magazine · 2025Article
- "Recon-all-clinical": Cortical surface reconstruction and analysis of heterogeneous clinical brain MRI.Medical image analysis · 2025Article
- CARL: A Framework for Equivariant Image Registration.Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition · 2025Article
- MultiMorph: On-demand Atlas Construction.Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition · 2025Article
Corrections and comments
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Authors and funding
1 author at 1 institution in 2 countries.
Funding
Abstract
Volumetric registration of brain MRI is routinely used in human neuroimaging, e.g., to align different MRI modalities, to measure change in longitudinal analysis, to map an individual to a template, or in registration-based segmentation. Classical registration techniques based on numerical optimization have been very successful in this domain, and are implemented in widespread software suites like ANTs, Elastix, NiftyReg, or DARTEL. Over the last 7-8 years, learning-based techniques have emerged, which have a number of advantages like high computational efficiency, potential for higher accuracy, easy integration of supervision, and the ability to be part of a meta-architectures. However, their adoption in neuroimaging pipelines has so far been almost inexistent. Reasons include: lack of robustness to changes in MRI modality and resolution; lack of robust affine registration modules; lack of (guaranteed) symmetry; and, at a more practical level, the requirement of deep learning expertise that may be lacking at neuroimaging research sites. Here, we present EasyReg, an open-source, learning-based registration tool that can be easily used from the command line without any deep learning expertise or specific hardware. EasyReg combines the features of classical registration tools, the capabilities of modern deep learning methods, and the robustness to changes in MRI modality and resolution provided by our recent work in domain randomization. As a result, EasyReg is: fast; symmetric; diffeomorphic (and thus invertible); agnostic to MRI modality and resolution; compatible with affine and nonlinear registration; and does not require any preprocessing or parameter tuning. We present results on challenging registration tasks, showing that EasyReg is as accurate as classical methods when registering 1 mm isotropic scans within MRI modality, but much more accurate across modalities and resolutions. EasyReg is publicly available as part of FreeSurfer; see https://surfer.nmr.mgh.harvard.edu/fswiki/EasyReg .
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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.