ArticleIEEE transactions on medical imaging2021
Weakly-Supervised Vessel Detection in Ultra-Widefield Fundus Photography via Iterative Multi-Modal Registration and Learning.
Article in IEEE transactions on medical imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed, 54 citations in OpenAlex.
- Self-supervised small vessel segmentation with shape-aware geometric models and attention.Medical image analysis · 2026Article
- 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
- COph100: A comprehensive fundus image registration dataset from infants constituting the "RIDIRP" database.Scientific data · 2025Article
- Universal Vessel Segmentation for Multi-Modality Retinal Images.IEEE transactions on image processing : a publication of the IEEE Signal Processing Society · 2025Article
- Open ultrawidefield fundus image dataset with disease diagnosis and clinical image quality assessment.Scientific data · 2024Article
- MEMO: dataset and methods for robust multimodal retinal image registration with large or small vessel density differences.Biomedical optics express · 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
- ReIU: an efficient preliminary framework for Alzheimer patients based on multi-model data.Frontiers in public health · 2024Article
- A multi-modal multi-branch framework for retinal vessel segmentation using ultra-widefield fundus photographs.Frontiers in cell and developmental biology · 2024Article
- ACCURATE REGISTRATION BETWEEN ULTRA-WIDE-FIELD AND NARROW ANGLE RETINA IMAGES WITH 3D EYEBALL SHAPE OPTIMIZATION.Proceedings. International Conference on Image Processing · 2023Article
- Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives.Medical image analysis · 2023Review
- Improving foveal avascular zone segmentation in fluorescein angiograms by leveraging manual vessel labels from public color fundus pictures.Biomedical optics express · 2022Article
- Spatiotemporal absorption fluctuation imaging based on U-Net.Journal of biomedical optics · 2022Article
- A Deep Learning System for Fully Automated Retinal Vessel Measurement in High Throughput Image Analysis.Frontiers in cardiovascular medicine · 2022Article
- Two-Step Registration on Multi-Modal Retinal Images via Deep Neural Networks.IEEE transactions on image processing : a publication of the IEEE Signal Processing Society · 2022Article
- Ultra-Widefield Fluorescein Angiography Image Brightness Compensation Based on Geometrical Features.Sensors (Basel, Switzerland) · 2021Article
- Perspective Distortion Correction for Multi-Modal Registration between Ultra-Widefield and Narrow-Angle Retinal Images.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2021Article
- Guest Editorial Annotation-Efficient Deep Learning: The Holy Grail of Medical Imaging.IEEE transactions on medical imaging · 2021Article
- Weakly-Supervised Vessel Detection in Ultra-Widefield Fundus Photography via Iterative Multi-Modal Registration and Learning.IEEE transactions on medical imaging · 2021Article
- Applications of Deep Learning: Automated Assessment of Vascular Tortuosity in Mouse Models of Oxygen-Induced Retinopathy.Ophthalmology scienceArticle
Corrections and comments
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Authors and funding
5 authors at 4 institutions in 1 country.
Funding
Abstract
We propose a deep-learning based annotation-efficient framework for vessel detection in ultra-widefield (UWF) fundus photography (FP) that does not require de novo labeled UWF FP vessel maps. Our approach utilizes concurrently captured UWF fluorescein angiography (FA) images, for which effective deep learning approaches have recently become available, and iterates between a multi-modal registration step and a weakly-supervised learning step. In the registration step, the UWF FA vessel maps detected with a pre-trained deep neural network (DNN) are registered with the UWF FP via parametric chamfer alignment. The warped vessel maps can be used as the tentative training data but inevitably contain incorrect (noisy) labels due to the differences between FA and FP modalities and the errors in the registration. In the learning step, a robust learning method is proposed to train DNNs with noisy labels. The detected FP vessel maps are used for the registration in the following iteration. The registration and the vessel detection benefit from each other and are progressively improved. Once trained, the UWF FP vessel detection DNN from the proposed approach allows FP vessel detection without requiring concurrently captured UWF FA images. We validate the proposed framework on a new UWF FP dataset, PRIME-FP20, and on existing narrow-field FP datasets. Experimental evaluation, using both pixel-wise metrics and the CAL metrics designed to provide better agreement with human assessment, shows that the proposed approach provides accurate vessel detection, without requiring manually labeled UWF FP training data.
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Registered trials
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