ArticleJournal of glaucoma2019
Optical Coherence Tomography Segmentation Errors of the Retinal Nerve Fiber Layer Persist Over Time.
Article in Journal of glaucoma, 2019. 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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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
13 citing papers in PubMed.
- Dynamic Oculomics for Diagnostic Monitoring: Personal Ocular Trends, Risk Stratification and Treatment Response.Diagnostics (Basel, Switzerland) · 2026Review
- The Application of a Deep Learning Algorithm for the Segmentation of Retinal Nerve Fiber Layer Across Different Optic Neuropathies.Translational vision science & technology · 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
- Factors influencing image quality in Tibetan children by optical coherence tomography.Frontiers in medicine · 2025Article
- Retinal Nerve Fiber Layer Damage Assessment in Glaucomatous Eyes Using Retinal Retardance Measured by Polarization-Sensitive Optical Coherence Tomography.Translational vision science & technology · 2024Article
- Measurement of retinal nerve fiber layer thickness with a deep learning algorithm in ischemic optic neuropathy and optic neuritis.Scientific reports · 2022Article
- Contrast-to-Noise Ratios to Evaluate the Detection of Glaucomatous Progression in the Superior and Inferior Hemiretina.Translational vision science & technology · 2022Article
- Detection of Early Glaucomatous Damage: Performance of Summary Statistics From Optical Coherence Tomography and Perimetry.Translational vision science & technology · 2022Article
- The OCT RNFL Probability Map and Artifacts Resembling Glaucomatous Damage.Translational vision science & technology · 2022Article
- Reasons why OCT Global Circumpapillary Retinal Nerve Fiber Layer Thickness is a Poor Measure of Glaucomatous Progression.Translational vision science & technology · 2020Article
- Optical Coherence Tomography Structural Abnormality Detection in Glaucoma Using Topographically Correspondent Rim and Retinal Nerve Fiber Layer Criteria.American journal of ophthalmology · 2020Article
- Evaluation of macular thickness and volume tested by optical coherence tomography as biomarkers for Alzheimer's disease in a memory clinic.Scientific reports · 2020Article
- Clinicians' Use of Quantitative Information When Assessing the Rate of Structural Progression in Glaucoma.Ophthalmology. GlaucomaArticle
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
PRéCIS:: There are errors in automated segmentation of the retinal nerve fiber layer (RNFL) in glaucoma suspects or patients with mild glaucoma that appear to persist over time; however, automated segmentation has greater repeatability than manual segmentation. PURPOSE: To identify whether optical coherence tomography (OCT) segmentation errors in RNFL thickness measurements persist longitudinally.
methodsThis was a cohort study. 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 RNFL thickness results after "manual refinement" to correct errors in the automated segmentation, and used the differences in these measurements as "error" in segmentation. We used Bland-Altman plots and linear regression to determine the magnitude, location, and repeatability of RNFL thickness error in all twelve 30-degree sectors and compared the error at baseline to follow-up time points at 6 months, 2 years, 3 years, and 4 years.
resultsWe included 406 eyes from 213 participants. The 95% confidence interval for errors at baseline was -6.5 to +13.2 μm. The correlation between the baseline error and the errors in the follow-up time periods were high (r>0.5, P<0.001 for all). Automated segmentation had a smaller SD of residuals from the longitudinal trend line when compared to manual refinement (1.56 vs. 1.80 μm, P<0.001), and a higher ability (P=0.009) to monitor progression using an analysis of a longitudinal signal-to-noise ratio.
conclusionsErrors in automated segmentation remain relatively stable, and baseline error is highly likely to persist in the same direction and magnitude in subsequent time periods. However, automated segmentation (without manual refinement) is more repeatable and may be more sensitive to glaucomatous progression. Future segmentation algorithms could exploit these findings to improve automated segmentation in the future.
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