ReviewFrontiers in oncology2022
A review of deep learning-based deformable medical image registration.
Review in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
23 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
- Automated workflow for target volume propagation in online adaptive magnetic resonance guided radiotherapy for lung lesions using foundation models.Physics and imaging in radiation oncology · 2026Article
- Group-Aware Registration for Lesion-Level Quantitative Motion Correction in Respiratory-Gated PET/CT Biomedical Imaging.Sensors (Basel, Switzerland) · 2026Article
- Review
- Transferring thrombus labels from CTPA to non-contrast CT via accurate pulmonary vessel registration: a critical step toward AI-based pulmonary embolism detection.Quantitative imaging in medicine and surgery · 2026Article
- Deep neural networks to register and annotate cells in moving and deforming nervous systems.eLife · 2026Article
- Article
- Improving data-driven gated (DDG) PET and CT registration in thoracic lesions: a comparison of AI registration and DDG CT.EJNMMI physics · 2025Article
- A conditional point cloud diffusion model for deformable liver motion tracking via a single arbitrarily-angled x-ray projection.Physics in medicine and biology · 2025Article
- PelviNet: A Collaborative Multi-agent Convolutional Network for Enhanced Pelvic Image Registration.Journal of imaging informatics in medicine · 2025Article
- A survey on deep learning in medical image registration: New technologies, uncertainty, evaluation metrics, and beyond.Medical image analysis · 2025Review
- Deformable registration and generative modelling of aortic anatomies by auto-decoders and neural ODEs.npj biological physics and mechanics · 2025Article
- A Physics-Informed Deep Learning Deformable Medical Image Registration Method Based on Neural ODEs.International journal of computer vision · 2025Article
- PViT-AIR: Puzzling vision transformer-based affine image registration for multi histopathology and faxitron images of breast tissue.Medical image analysis · 2025Article
- Respiratory motion-corrected T1 mapping of the abdomen.Magma (New York, N.Y.) · 2024Article
- Deformable registration of magnetic resonance images using unsupervised deep learning in neuro-/radiation oncology.Radiation oncology (London, England) · 2024Article
- Recent Advances in Endoscopic Ultrasound for Gallbladder Disease Diagnosis.Diagnostics (Basel, Switzerland) · 2024Review
- Three-Dimensional Multi-Modality Registration for Orthopaedics and Cardiovascular Settings: State-of-the-Art and Clinical Applications.Sensors (Basel, Switzerland) · 2024Article
- Review and recommendations on deformable image registration uncertainties for radiotherapy applications.Physics in medicine and biology · 2023Review
- Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The alignment of images through deformable image registration is vital to clinical applications (e.g., atlas creation, image fusion, and tumor targeting in image-guided navigation systems) and is still a challenging problem. Recent progress in the field of deep learning has significantly advanced the performance of medical image registration. In this review, we present a comprehensive survey on deep learning-based deformable medical image registration methods. These methods are classified into five categories: Deep Iterative Methods, Supervised Methods, Unsupervised Methods, Weakly Supervised Methods, and Latest Methods. A detailed review of each category is provided with discussions about contributions, tasks, and inadequacies. We also provide statistical analysis for the selected papers from the point of view of image modality, the region of interest (ROI), evaluation metrics, and method categories. In addition, we summarize 33 publicly available datasets that are used for benchmarking the registration algorithms. Finally, the remaining challenges, future directions, and potential trends are discussed in our review.
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What OpenQuestion holds
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.