ArticleIEEE transactions on image processing : a publication of the IEEE Signal Processing Society2022
Two-Step Registration on Multi-Modal Retinal Images via Deep Neural Networks.
Article in IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Generalizable Multimodal Retinal Image Registration via Label-free Vessel Segmentation.Biomedical signal processing and control · 2026Article
- On the degrees of freedom of gridded control points in learning-based medical image registration.Medical physics · 2026Article
- Oculomics: advances and perspectives from traditional Chinese medicine to modern multimodal biomarkers.International journal of ophthalmology · 2026Review
- Cross-modality synthesis of ultra-widefield fluorescein angiography from ultra-widefield color fundus photography for diabetic retinopathy via UWFDR-GAN.Journal of translational medicine · 2025Article
- A coarse-to-fine registration method for multimodal retinal images.Scientific reports · 2025Article
- A Novel Multi-Source Image Registration of Porcine Body for Multi-Feature Detection.Sensors (Basel, Switzerland) · 2025Article
- RetinaRegNet: A zero-shot approach for retinal image registration.Computers in biology and medicine · 2025Article
- Universal Vessel Segmentation for Multi-Modality Retinal Images.IEEE transactions on image processing : a publication of the IEEE Signal Processing Society · 2025Article
- Artificial intelligence technology in ophthalmology public health: current applications and future directions.Frontiers in cell and developmental biology · 2025Review
- A Rapid Head Organ Localization System Based on Clinically Realistic Images: A 3D Two Step Progressive Registration Method with CVH Anatomical Knowledge Mapping.Bioengineering (Basel, Switzerland) · 2024Article
- Medical image registration and its application in retinal images: a review.Visual computing for industry, biomedicine, and art · 2024Review
- Enhanced multimodal medical image fusion via modified DWT with arithmetic optimization algorithm.Scientific reports · 2024Article
- MEMO: dataset and methods for robust multimodal retinal image registration with large or small vessel density differences.Biomedical optics express · 2024Article
- L2NLF: a novel linear-to-nonlinear framework for multi-modal medical image registration.Biomedical engineering letters · 2024Article
- Ultra-wide field and new wide field composite retinal image registration with AI-enabled pipeline and 3D distortion correction algorithm.Eye (London, England) · 2024Article
- A robust and interpretable deep learning framework for multi-modal registration via keypoints.Medical image analysis · 2023Article
- ACCURATE REGISTRATION BETWEEN ULTRA-WIDE-FIELD AND NARROW ANGLE RETINA IMAGES WITH 3D EYEBALL SHAPE OPTIMIZATION.Proceedings. International Conference on Image Processing · 2023Article
- Efficacy and accuracy of artificial intelligence to overlay multimodal images from different optical instruments in patients with retinitis pigmentosa.Clinical & experimental ophthalmology · 2023Article
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Authors and funding
8 authors.
Funding
Abstract
Multi-modal retinal image registration plays an important role in the ophthalmological diagnosis process. The conventional methods lack robustness in aligning multi-modal images of various imaging qualities. Deep-learning methods have not been widely developed for this task, especially for the coarse-to-fine registration pipeline. To handle this task, we propose a two-step method based on deep convolutional networks, including a coarse alignment step and a fine alignment step. In the coarse alignment step, a global registration matrix is estimated by three sequentially connected networks for vessel segmentation, feature detection and description, and outlier rejection, respectively. In the fine alignment step, a deformable registration network is set up to find pixel-wise correspondence between a target image and a coarsely aligned image from the previous step to further improve the alignment accuracy. Particularly, an unsupervised learning framework is proposed to handle the difficulties of inconsistent modalities and lack of labeled training data for the fine alignment step. The proposed framework first changes multi-modal images into a same modality through modality transformers, and then adopts photometric consistency loss and smoothness loss to train the deformable registration network. The experimental results show that the proposed method achieves state-of-the-art results in Dice metrics and is more robust in challenging cases.
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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.