ArticleBiomedical optics express2021
Segregation of neuronal-vascular components in a retinal nerve fiber layer for thickness measurement using OCT and OCT angiography.
Article in Biomedical optics express, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
What it found
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 13 citations in OpenAlex.
- Deep learning and optical coherence tomography in glaucoma: Bridging the diagnostic gap on structural imaging.Frontiers in ophthalmology · 2022Pooled it
- SYN-OCT:A synthetic dataset of ocular optical coherence tomography images from healthy and glaucoma eyes.Scientific data · 2026Article
- Nonaxonal content of the circumpapillary retinal nerve fiber layer in experimental glaucoma.Optometry and vision science : official publication of the American Academy of Optometry · 2026Article
- Advances in ocular aging: combining deep learning, imaging, and liquid biopsy biomarkers.Frontiers in medicine · 2025Review
- Association of macular vessel density and ganglion cell complex thickness with central visual field progression in glaucoma.The British journal of ophthalmology · 2023Article
- Foveal avascular zone vessel density is associated with visual field progression in early-stage glaucoma eyes with central visual field damage.Scientific reports · 2023Article
- Measurement of retinal nerve fiber layer thickness with a deep learning algorithm in ischemic optic neuropathy and optic neuritis.Scientific reports · 2022Article
- Comparison of machine learning approaches for structure-function modeling in glaucoma.Annals of the New York Academy of Sciences · 2022Article
- Combining OCT and OCTA for Focal Structure-Function Modeling in Early Primary Open-Angle Glaucoma.Investigative ophthalmology & visual science · 2021Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Assessment of the circumpapillary retinal nerve fiber layer (RNFL) provides crucial knowledge on the status of the optic nerve. Current circumpapillary RNFL measurements consider only thickness, but an accurate evaluation should also consider blood vessel contribution. Previous studies considered the presence of major vessels in RNFL thickness measurements from optical coherence tomography (OCT). However, such quantitative measurements do not account for smaller vessels, which could also affect circumpapillary RNFL measurements. We present an approach to automatically segregate the neuronal and vascular components in circumpapillary RNFL by combining vascular information from OCT angiography (OCTA) and structural data from OCT. Automated segmentation of the circumpapillary RNFL using a state-of-the-art deep learning network is first performed and followed by the lateral and depth-resolved localization of the vascular component by vertically projecting the vessels along the circular scan from OCTA vessels map onto the segmented RNFL. Using this proposed approach, we compare the correlations of circumpapillary RNFL thicknesses with age at different levels of vessel exclusion (exclusion of major vessels only vs both major- and micro-vessels) and also evaluate the thickness variability in 75 healthy eyes. Our results show that the ratio of major- and micro-vessels to circumpapillary RNFL achieved a stronger correlation with aging (r = 0.478,
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Registered trials
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