ReviewQuantitative imaging in medicine and surgery2021
A review of deep learning-based three-dimensional medical image registration methods.
Review in Quantitative imaging in medicine and surgery, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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.
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Who cites it
27 citing papers in PubMed.
- A label-aware diffusion model for weakly supervised deformable registration of multimodal MRI-TRUS in prostate cancer.International journal of computer assisted radiology and surgery · 2026Article
- Fluoroscopy-Guided Motion Management in Particle Therapy: Evolution, Challenges, and AI-Enabled Opportunities.Tomography (Ann Arbor, Mich.) · 2026Review
- Unsupervised cycle-consistent network for 3D pelvic mono- or multimodal deformable image registration.Radiation oncology (London, England) · 2025Article
- From traditional to deep learning approaches in whole slide image registration: A methodological review.Journal of pathology informatics · 2025Review
- Enhancing unsupervised learning in medical image registration through scale-aware context aggregation.iScience · 2025Article
- A survey on deep learning in medical image registration: New technologies, uncertainty, evaluation metrics, and beyond.Medical image analysis · 2025Review
- Artificial Intelligence-Empowered Radiology-Current Status and Critical Review.Diagnostics (Basel, Switzerland) · 2025Review
- Development and validation of a multi-parametric MRI deep-learning model for preoperative lymphovascular invasion evaluation in rectal cancer.Quantitative imaging in medicine and surgery · 2025Article
- Orthopedic Trainees' Perception of the Educational Utility of Patient-Specific 3D-Printed Anatomical Models: A Questionnaire-Based Observational Study.Advances in medical education and practice · 2025Article
- ConKeD: multiview contrastive descriptor learning for keypoint-based retinal image registration.Medical & biological engineering & computing · 2024Article
- Article
- Deep learning-based deformable image registration with bilateral pyramid to align pre-operative and follow-up magnetic resonance imaging (MRI) scans.Quantitative imaging in medicine and surgery · 2024Article
- Deep learning-based target tracking with X-ray images for radiotherapy: a narrative review.Quantitative imaging in medicine and surgery · 2024Review
- LowQuantitative imaging in medicine and surgery · 2024Article
- Review and recommendations on deformable image registration uncertainties for radiotherapy applications.Physics in medicine and biology · 2023Review
- A deformable patch-based transformer for 3D medical image registration.International journal of computer assisted radiology and surgery · 2023Article
- An overview of artificial intelligence in medical physics and radiation oncology.Journal of the National Cancer Center · 2023Review
- Joint keypoint detection and description network for color fundus image registration.Quantitative imaging in medicine and surgery · 2023Article
- [Non-rigid registration for medical images based on deformable convolution and multi-scale feature focusing modules].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2023Article
- Impact of deep learning-based image reconstruction on image quality and lesion visibility in renal computed tomography at different doses.Quantitative imaging in medicine and surgery · 2023Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Medical image registration is a vital component of many medical procedures, such as image-guided radiotherapy (IGRT), as it allows for more accurate dose-delivery and better management of side effects. Recently, the successful implementation of deep learning (DL) in various fields has prompted many research groups to apply DL to three-dimensional (3D) medical image registration. Several of these efforts have led to promising results. This review summarized the progress made in DL-based 3D image registration over the past 5 years and identify existing challenges and potential avenues for further research. The collected studies were statistically analyzed based on the region of interest (ROI), image modality, supervision method, and registration evaluation metrics. The studies were classified into three categories: deep iterative registration, supervised registration, and unsupervised registration. The studies are thoroughly reviewed and their unique contributions are highlighted. A summary is presented following a review of each category of study, discussing its advantages, challenges, and trends. Finally, the common challenges for all categories are discussed, and potential future research topics are identified.
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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.