ArticleMedical image analysis2018
Weakly-supervised convolutional neural networks for multimodal image registration.
Article in Medical image analysis, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 120 papers.
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Who cites it
120 citing papers in PubMed.
- Deep Learning-based Non-rigid Image Registration for High-dose Rate Brachytherapy in Inter-fraction Cervical Cancer.Journal of digital imaging · 2023Trial
- Robust cross-anatomy image registration leveraging keypoint priors and contour-based assessment.Medical & biological engineering & computing · 2026Article
- 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
- GuidedMorph: Two-Stage Deformable Registration for Breast MRI.IEEE journal of biomedical and health informatics · 2026Article
- A feasibility study on multimodal CT-MRI registration using segmentation aid and CoLlAGe feature extraction approach.Abdominal radiology (New York) · 2026Article
- Assessing the suitability of automated registration and segmentation for dosimetry calculations in SIRT treatment planning.EJNMMI physics · 2026Article
- Fluoroscopy-Guided Motion Management in Particle Therapy: Evolution, Challenges, and AI-Enabled Opportunities.Tomography (Ann Arbor, Mich.) · 2026Review
- 3D-WDA-PMorph: Efficient 3D MRI/TRUS Prostate Registration using Transformer-CNN Network and Wavelet-3D-Depthwise-Attention.Journal of imaging informatics in medicine · 2026Article
- Shared and divergent alteration of whole-brain connectivity and sensory deficits in multiple autism mouse models.Molecular psychiatry · 2026Article
- Memory efficient training for 3D brain image registration networks using PatchMorph.Scientific reports · 2026Article
- Image registration using MR-based synthetic CT (sCT) generated by cycle-consistent adversarial networks.Biomedical engineering letters · 2026Article
- Tumor-conditioned inter-patient registration using planning computed tomography for voxel-based analysis to predict radiation pneumonitis in lung cancer patients.Physics and imaging in radiation oncology · 2026Article
- A Review on Registration Techniques for Cardiac Computed Tomography and Ultrasound Images.Bioengineering (Basel, Switzerland) · 2025Review
- Deformation-Aware MR-TRUS Image Translation for Prostate Cancer Brachytherapy.Research square · 2025Article
- Article
- DINO-Reg: Efficient Multimodal Image Registration With Distilled Features.IEEE transactions on medical imaging · 2025Article
- E-CMCA and LSTM-Enhanced Framework for Cross-Modal MRI-TRUS Registration in Prostate Cancer.Journal of imaging · 2025Article
- Uncertainty Estimation for Dual View X-ray Mammographic Image Registration Using Deep Ensembles.Journal of imaging informatics in medicine · 2025Article
- IR-GPT: AI Foundation Models to Optimize Interventional Radiology.Cardiovascular and interventional radiology · 2025Review
- Towards automatic US-MR fetal brain image registration with learning-based methods.NeuroImage · 2025Article
60 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
14 authors.
Funding
Abstract
One of the fundamental challenges in supervised learning for multimodal image registration is the lack of ground-truth for voxel-level spatial correspondence. This work describes a method to infer voxel-level transformation from higher-level correspondence information contained in anatomical labels. We argue that such labels are more reliable and practical to obtain for reference sets of image pairs than voxel-level correspondence. Typical anatomical labels of interest may include solid organs, vessels, ducts, structure boundaries and other subject-specific ad hoc landmarks. The proposed end-to-end convolutional neural network approach aims to predict displacement fields to align multiple labelled corresponding structures for individual image pairs during the training, while only unlabelled image pairs are used as the network input for inference. We highlight the versatility of the proposed strategy, for training, utilising diverse types of anatomical labels, which need not to be identifiable over all training image pairs. At inference, the resulting 3D deformable image registration algorithm runs in real-time and is fully-automated without requiring any anatomical labels or initialisation. Several network architecture variants are compared for registering T2-weighted magnetic resonance images and 3D transrectal ultrasound images from prostate cancer patients. A median target registration error of 3.6 mm on landmark centroids and a median Dice of 0.87 on prostate glands are achieved from cross-validation experiments, in which 108 pairs of multimodal images from 76 patients were tested with high-quality anatomical labels.
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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.