ArticleAmerican journal of ophthalmology2017
Automated Segmentation Errors When Using Optical Coherence Tomography to Measure Retinal Nerve Fiber Layer Thickness in Glaucoma.
Article in American journal of ophthalmology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers.
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Who cites it
61 citing papers in PubMed, 100 citations in OpenAlex.
- AI-driven multimodal retinal imaging for early detection and risk stratification of vascular and neurodegenerative diseases.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Review
- Inter-individual biological variability of retinal layer thickness on optical coherence tomography scans.International ophthalmology · 2026Article
- Artificial Intelligence in Glaucoma Detection and Progression.Advances in ophthalmology and optometry · 2026Article
- Effect of Signal-Strength Filtering on 3D Convolutional Neural Network-Based Visual Field Estimation From Macular OCT.Translational vision science & technology · 2026Article
- Short-Term Impact of Scleral Lens Wear on Intraocular Pressure and Retinal Nerve Fiber Layer Thickness.Life (Basel, Switzerland) · 2026Article
- Rhesus macaques with anProceedings of the National Academy of Sciences of the United States of America · 2026Article
- Association of polysomnography findings of obstructive sleep apnea with non-arteritic anterior ischemic optic neuropathy manifestations.Sleep & breathing = Schlaf & Atmung · 2026Article
- The Application of a Deep Learning Algorithm for the Segmentation of Retinal Nerve Fiber Layer Across Different Optic Neuropathies.Translational vision science & technology · 2026Article
- Developing Clinically Implementable Approaches for Directional Optical Coherence Tomography.Translational vision science & technology · 2026Article
- Target-label-free artificial intelligence framework for cross-anatomical RNFL biomarker segmentation in optical coherence tomography.Frontiers in cell and developmental biology · 2026Article
- Quantitative Analysis of Retinal Nerve Fiber Layer Thickness Using SPECTRALIS Glaucoma Module Premium Edition and SPECTRALIS Standard Glaucoma Module.Translational vision science & technology · 2025Article
- The Effects of Disease-Modifying Therapies on Optic Nerve Degeneration in Multiple Sclerosis.European journal of neurology · 2025Review
- Wide-field OCT volumetric segmentation using semi-supervised CNN and transformer integration.Scientific reports · 2025Article
- Factors influencing image quality in Tibetan children by optical coherence tomography.Frontiers in medicine · 2025Article
- Intraday repeatability of macular layers measurements in glaucomatous and non-glaucomatous patients using spectral-domain optical coherence tomography.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2024Article
- High Prevalence of Artifacts in Optical Coherence Tomography With Adequate Signal Strength.Translational vision science & technology · 2024Article
- Retinal Nerve Fiber Layer Damage Assessment in Glaucomatous Eyes Using Retinal Retardance Measured by Polarization-Sensitive Optical Coherence Tomography.Translational vision science & technology · 2024Article
- The Temporal Relation Between Rates of Retinal Nerve Fiber Layer and Minimum Rim Width Changes in Glaucoma.Translational vision science & technology · 2024Article
- Assessment of Time Lag Between Blood Flow, Retinal Nerve Fiber Layer Thickness and Visual Field Sensitivity Changes in Glaucoma.Investigative ophthalmology & visual science · 2024Article
- Measurement of retinal nerve fiber layer thickness with a deep learning algorithm in ischemic optic neuropathy and optic neuritis.Scientific reports · 2022Article
1 more citing papers are in PubMed but not listed here.
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
purposeTo characterize the error of optical coherence tomography (OCT) measurements of retinal nerve fiber layer (RNFL) thickness when using automated retinal layer segmentation algorithms without manual refinement.
designCross-sectional study.
methodsThis study was set in a glaucoma clinical practice, and the dataset included 3490 scans from 412 eyes of 213 individuals with a diagnosis of glaucoma or glaucoma suspect. We used spectral domain OCT (Spectralis) to measure RNFL thickness in a 6-degree peripapillary circle, and exported the native "automated segmentation only" results. In addition, we exported the results after "manual refinement" to correct errors in the automated segmentation of the anterior (internal limiting membrane) and the posterior boundary of the RNFL. Our outcome measures included differences in RNFL thickness and glaucoma classification (i.e., normal, borderline, or outside normal limits) between scans with automated segmentation only and scans using manual refinement.
resultsAutomated segmentation only resulted in a thinner global RNFL thickness (1.6 μm thinner, P < .001) when compared to manual refinement. When adjusted by operator, a multivariate model showed increased differences with decreasing RNFL thickness (P < .001), decreasing scan quality (P < .001), and increasing age (P < .03). Manual refinement changed 298 of 3486 (8.5%) of scans to a different global glaucoma classification, wherein 146 of 617 (23.7%) of borderline classifications became normal. Superior and inferior temporal clock hours had the largest differences.
conclusionsAutomated segmentation without manual refinement resulted in reduced global RNFL thickness and overestimated the classification of glaucoma. Differences increased in eyes with a thinner RNFL thickness, older age, and decreased scan quality. Operators should inspect and manually refine OCT retinal layer segmentation when assessing RNFL thickness in the management of patients with glaucoma.
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