ArticleMedical image analysis2022
TransMorph: Transformer for unsupervised medical image registration.
Article in Medical image analysis, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 132 papers, 2 of them syntheses that pooled it.
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Who cites it
132 citing papers in PubMed, 2 syntheses or guidelines pooled it, 653 citations in OpenAlex.
- Applying transformer-based deep learning models in image-driven cancer diagnosis: a comprehensive bibliometric analysis of global research trends.Frontiers in oncology · 2026Pooled it
- Systematic Review of Hybrid Vision Transformer Architectures for Radiological Image Analysis.Journal of imaging informatics in medicine · 2025Pooled it
- AI in Proton Therapy Treatment Planning: A Narrative Review.International journal of particle therapy · 2026Article
- Comparative evaluation of VoxelMorpand conventional deformable image registration algorithms for thoracic 4D-CT in radiotherapy.Journal of applied clinical medical physics · 2026Article
- MRI-derived habitat radiomics integrated with transformer deep learning for preoperative prediction of high CD9 expression: a novel prognostic marker in adult-type diffuse gliomas.Translational cancer research · 2026Article
- Topology-preserved transformer-based CBCT-CT registration via normalized gradient field guidance for nasopharyngeal carcinoma.iScience · 2026Article
- MSDW-Net: A Multi-Scale Network for Medical Image Registration Using Dynamic Wavelet Transform.Interdisciplinary sciences, computational life sciences · 2026Article
- 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
- Contour-regulated registration framework for Liver CT perfusion images.Medical physics · 2026Article
- Beyond the LUMIR challenge: The pathway to foundational registration models.Medical image analysis · 2026Article
- BioDeformUNet: A Deep Learning Model for Biomechanically Informed Liver Image Registration.medRxiv : the preprint server for health sciences · 2026Article
- Understanding Trade-Offs in Continuous Neural Representations for Diffeomorphic Image Registration: A Comparative Study of Implicit Neural Representations and Neural Ordinary Differential Equations.Journal of imaging · 2026Article
- Patient-specific automated multi-class anatomical motion tracking on real-time cine MR images using deep learning techniques.Medical physics · 2026Article
- W-STFNet: A Wavelet Transform-Based Regularized Hybrid Recursive Spatiotemporal Fusion Registration Network.Annals of biomedical engineering · 2026Article
- VITAL: Value-Invariant Transformation and Alignment Learning for quantitative photoacoustic microscopy.Photoacoustics · 2026Article
- Group-Aware Registration for Lesion-Level Quantitative Motion Correction in Respiratory-Gated PET/CT Biomedical Imaging.Sensors (Basel, Switzerland) · 2026Article
- One-for-All Adaptive Radiotherapy Planning Agent: A Foundation Framework for Daily CBCT-guided Radiotherapy.ArXiv · 2026Article
- Large-scale multi-sequence pretraining for generalizable MRI analysis in versatile clinical applications.Nature biomedical engineering · 2026Article
- DSHARP: Deep Incompressible Motion Estimation With Sinusoidal-Transformed Harmonic Phase for Tagged MRI.IEEE transactions on medical imaging · 2026Article
- GuidedMorph: Two-Stage Deformable Registration for Breast MRI.IEEE journal of biomedical and health informatics · 2026Article
72 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
6 authors at 4 institutions in 2 countries.
Funding
Abstract
In the last decade, convolutional neural networks (ConvNets) have been a major focus of research in medical image analysis. However, the performances of ConvNets may be limited by a lack of explicit consideration of the long-range spatial relationships in an image. Recently, Vision Transformer architectures have been proposed to address the shortcomings of ConvNets and have produced state-of-the-art performances in many medical imaging applications. Transformers may be a strong candidate for image registration because their substantially larger receptive field enables a more precise comprehension of the spatial correspondence between moving and fixed images. Here, we present TransMorph, a hybrid Transformer-ConvNet model for volumetric medical image registration. This paper also presents diffeomorphic and Bayesian variants of TransMorph: the diffeomorphic variants ensure the topology-preserving deformations, and the Bayesian variant produces a well-calibrated registration uncertainty estimate. We extensively validated the proposed models using 3D medical images from three applications: inter-patient and atlas-to-patient brain MRI registration and phantom-to-CT registration. The proposed models are evaluated in comparison to a variety of existing registration methods and Transformer architectures. Qualitative and quantitative results demonstrate that the proposed Transformer-based model leads to a substantial performance improvement over the baseline methods, confirming the effectiveness of Transformers for medical image registration.
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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.