ArticleNeuroImage2017
Quicksilver: Fast predictive image registration - A deep learning approach.
Article in NeuroImage, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 175 papers, 1 of them a synthesis that pooled it.
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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
175 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Review on the Use of Registration-Based Change Tracking Methods in Longitudinal Radiological Images.Journal of imaging informatics in medicine · 2025Pooled it
- Deep Learning-based Non-rigid Image Registration for High-dose Rate Brachytherapy in Inter-fraction Cervical Cancer.Journal of digital imaging · 2023Trial
- AI in Proton Therapy Treatment Planning: A Narrative Review.International journal of particle therapy · 2026Article
- Deep Learning Based Cross-Modality Histological Brain Section Registration in Multiple Species Using Synthetic Images.bioRxiv : the preprint server for biology · 2026Article
- Evaluating LED parameters in a growth chamber to maintain potato yield and enhance minitubers production in greenhouse with molecular insights.Scientific reports · 2026Article
- On the accuracy of image registration in portable low-field 3D brain MRI.Research square · 2026Article
- On the accuracy of image registration in portable low-field 3D brain MRI.bioRxiv : the preprint server for biology · 2026Article
- Unified Brain Surface and Volume Registration.... International Conference on Learning Representations · 2026Article
- Solving the where problem and quantifying geometric variation in neuroanatomy using generative diffeomorphic mapping.Nature communications · 2025Article
- IntegrAlign: a comprehensive tool for multi-immunofluorescence panel integration through image alignment.Bioinformatics (Oxford, England) · 2025Article
- Article
- Optimising Multimodal Image Registration Techniques: A Comprehensive Study of Non-Rigid and Affine Methods for PET/CT Integration.Diagnostics (Basel, Switzerland) · 2025Article
- [AI-based applications in medical image computing].Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz · 2025Review
- Uncertainty Quantification in Image-based 2D/3D Registration and Its Relationship with Accuracy.International journal of computer assisted radiology and surgery · 2025Article
- CARL: A Framework for Equivariant Image Registration.Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition · 2025Article
- Unraveling Normal Anatomy via Fluid-Driven Anomaly Randomization.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
- Uncertainty Estimation for Dual View X-ray Mammographic Image Registration Using Deep Ensembles.Journal of imaging informatics in medicine · 2025Article
- TRUSTED: The Paired 3D Transabdominal Ultrasound and CT Human Data for Kidney Segmentation and Registration Research.Scientific data · 2025Article
- Wavelet-Guided Multi-Scale ConvNeXt for Unsupervised Medical Image Registration.Bioengineering (Basel, Switzerland) · 2025Article
115 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
This paper introduces Quicksilver, a fast deformable image registration method. Quicksilver registration for image-pairs works by patch-wise prediction of a deformation model based directly on image appearance. A deep encoder-decoder network is used as the prediction model. While the prediction strategy is general, we focus on predictions for the Large Deformation Diffeomorphic Metric Mapping (LDDMM) model. Specifically, we predict the momentum-parameterization of LDDMM, which facilitates a patch-wise prediction strategy while maintaining the theoretical properties of LDDMM, such as guaranteed diffeomorphic mappings for sufficiently strong regularization. We also provide a probabilistic version of our prediction network which can be sampled during the testing time to calculate uncertainties in the predicted deformations. Finally, we introduce a new correction network which greatly increases the prediction accuracy of an already existing prediction network. We show experimental results for uni-modal atlas-to-image as well as uni-/multi-modal image-to-image registrations. These experiments demonstrate that our method accurately predicts registrations obtained by numerical optimization, is very fast, achieves state-of-the-art registration results on four standard validation datasets, and can jointly learn an image similarity measure. Quicksilver is freely available as an open-source software.
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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.