ArticleMedical image analysis2023
A robust and interpretable deep learning framework for multi-modal registration via keypoints.
Article in Medical image analysis, 2023. 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.
- VITAL: Value-Invariant Transformation and Alignment Learning for quantitative photoacoustic microscopy.Photoacoustics · 2026Article
- [A reweighted graph matching-based algorithm for automatic extraction of corresponding landmarks in medical images].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2026Article
- Review
- CONReg: Uncertainty-Aware Medical Image Registration Using Conformal Prediction.Journal of imaging informatics in medicine · 2026Article
- From Geometry to Intensity: A Coarse-to-Fine Pipeline for Unsupervised 3D Ultrasound Stitching.Proceedings of SPIE--the International Society for Optical Engineering · 2026Article
- BigReg: an efficient registration pipeline for high-resolution X-ray and light-sheet fluorescence microscopy.Journal of medical imaging (Bellingham, Wash.) · 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
- Towards automatic US-MR fetal brain image registration with learning-based methods.NeuroImage · 2025Article
- Hierarchical uncertainty estimation for learning-based registration in neuroimaging.... International Conference on Learning Representations · 2025Article
- Intelligent detection and grading diagnosis of fresh rib fractures based on deep learning.BMC medical imaging · 2025Article
- SE(3)-Equivariant and Noise-Invariant 3D Rigid Motion Tracking in Brain MRI.IEEE transactions on medical imaging · 2024Article
- Registration by Regression (RbR): a framework for interpretable and flexible atlas registration.Biomedical image registration, ... proceedings. WBIR (Workshop : 2006- ) · 2024Article
- Anatomy-aware and acquisition-agnostic joint registration with SynthMorph.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- EyeLiner: A Deep Learning Pipeline for Longitudinal Image Registration Using Fundus Landmarks.Ophthalmology scienceArticle
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
We present KeyMorph, a deep learning-based image registration framework that relies on automatically detecting corresponding keypoints. State-of-the-art deep learning methods for registration often are not robust to large misalignments, are not interpretable, and do not incorporate the symmetries of the problem. In addition, most models produce only a single prediction at test-time. Our core insight which addresses these shortcomings is that corresponding keypoints between images can be used to obtain the optimal transformation via a differentiable closed-form expression. We use this observation to drive the end-to-end learning of keypoints tailored for the registration task, and without knowledge of ground-truth keypoints. This framework not only leads to substantially more robust registration but also yields better interpretability, since the keypoints reveal which parts of the image are driving the final alignment. Moreover, KeyMorph can be designed to be equivariant under image translations and/or symmetric with respect to the input image ordering. Finally, we show how multiple deformation fields can be computed efficiently and in closed-form at test time corresponding to different transformation variants. We demonstrate the proposed framework in solving 3D affine and spline-based registration of multi-modal brain MRI scans. In particular, we show registration accuracy that surpasses current state-of-the-art methods, especially in the context of large displacements. Our code is available at https://github.com/alanqrwang/keymorph.
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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.