ArticleBiomedical optics express2022
MF-AV-Net: an open-source deep learning network with multimodal fusion options for artery-vein segmentation in OCT angiography.
Article in Biomedical optics express, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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Who cites it
13 citing papers in PubMed, 36 citations in OpenAlex.
- Artificial Intelligence Applications in Sickle Cell Retinopathy Imaging: Current Progress, Challenges, and Future Directions.Journal of ophthalmology · 2026Review
- OCTA-ReVABiomedical optics express · 2025Article
- Widefield OCT angiography.Progress in retinal and eye research · 2025Review
- Deep learning segmentation of periarterial and perivenous capillary-free zones in optical coherence tomography angiography.Journal of biomedical optics · 2025Article
- Differential artery-vein analysis in OCTA for predicting the anti-VEGF treatment outcome of diabetic macular edema.Biomedical optics express · 2025Article
- Advances in OCT Angiography.Translational vision science & technology · 2025Review
- Differential Capillary and Large Vessel Analysis Improves OCTA Classification of Diabetic Retinopathy.Investigative ophthalmology & visual science · 2024Article
- Differential artery-vein analysis improves the OCTA classification of diabetic retinopathy.Biomedical optics express · 2024Article
- OCT-angiography based artificial intelligence-inferred fluorescein angiography for leakage detection in retina [Invited].Biomedical optics express · 2023Article
- Optimizing the OCTA layer fusion option for deep learning classification of diabetic retinopathy.Biomedical optics express · 2023Article
- Deep learning for artery-vein classification in optical coherence tomography angiography.Experimental biology and medicine (Maywood, N.J.) · 2023Review
- An open-source deep learning network AVA-Net for arterial-venous area segmentation in optical coherence tomography angiography.Communications medicine · 2023Article
- Response of Diabetic Macular Edema to Anti-VEGF Medications Correlates with Improvement in Macular Vessel Architecture Measured with OCT Angiography.Ophthalmology scienceArticle
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
This study is to demonstrate the effect of multimodal fusion on the performance of deep learning artery-vein (AV) segmentation in optical coherence tomography (OCT) and OCT angiography (OCTA); and to explore OCT/OCTA characteristics used in the deep learning AV segmentation. We quantitatively evaluated multimodal architectures with early and late OCT-OCTA fusions, compared to the unimodal architectures with OCT-only and OCTA-only inputs. The OCTA-only architecture, early OCT-OCTA fusion architecture, and late OCT-OCTA fusion architecture yielded competitive performances. For the 6 mm×6 mm and 3 mm×3 mm datasets, the late fusion architecture achieved an overall accuracy of 96.02% and 94.00%, slightly better than the OCTA-only architecture which achieved an overall accuracy of 95.76% and 93.79%. 6 mm×6 mm OCTA images show AV information at pre-capillary level structure, while 3 mm×3 mm OCTA images reveal AV information at capillary level detail. In order to interpret the deep learning performance, saliency maps were produced to identify OCT/OCTA image characteristics for AV segmentation. Comparative OCT and OCTA saliency maps support the capillary-free zone as one of the possible features for AV segmentation in OCTA. The deep learning network MF-AV-Net used in this study is available on GitHub for open access.
Identifiers
What Socratic holds
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 Socratic graph.