ArticleProceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition2023
GradICON: Approximate Diffeomorphisms via Gradient Inverse Consistency.
Article in Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Beyond the LUMIR challenge: The pathway to foundational registration models.Medical image analysis · 2026Article
- VITAL: Value-Invariant Transformation and Alignment Learning for quantitative photoacoustic microscopy.Photoacoustics · 2026Article
- Hyperelastic Regularization for Near-Diffeomorphic Transformer-Based Brain MRI Registration.Journal of imaging · 2026Article
- CoRe: Joint Optimization with Contrastive Learning for Medical Image Registration.Sensors (Basel, Switzerland) · 2026Article
- Emphysema progression risk in COPD using a localized foundational model of density evolution.NPJ digital medicine · 2025Article
- CARL: A Framework for Equivariant Image Registration.Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition · 2025Article
- A survey on deep learning in medical image registration: New technologies, uncertainty, evaluation metrics, and beyond.Medical image analysis · 2025Review
- Comprehensive Review: Machine and Deep Learning in Brain Stroke Diagnosis.Sensors (Basel, Switzerland) · 2024Review
- uniGradICON: A Foundation Model for Medical Image Registration.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2024Article
- Virtual and Augmented Reality in Interventional Radiology: Current Applications, Challenges, and Future Directions.Techniques in vascular and interventional radiology · 2023Article
- A ready-to-use machine learning tool for symmetric multi-modality registration of brain MRI.Scientific reports · 2023Article
- Inverse Consistency by Construction for Multistep Deep Registration.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2023Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
We present an approach to learning regular spatial transformations between image pairs in the context of medical image registration. Contrary to optimization-based registration techniques and many modern learning-based methods, we do not directly penalize transformation irregularities but instead promote transformation regularity via an inverse consistency penalty. We use a neural network to predict a map between a source and a target image as well as the map when swapping the source and target images. Different from existing approaches, we compose these two resulting maps and regularize deviations of the Jacobian of this composition from the identity matrix. This regularizer - GradICON - results in much better convergence when training registration models compared to promoting inverse consistency of the composition of maps directly while retaining the desirable implicit regularization effects of the latter. We achieve state-of-the-art registration performance on a variety of real-world medical image datasets using a single set of hyperparameters and a single non-dataset-specific training protocol. Code is available at https://github.com/uncbiag/ICON.
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Registered trials
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