Dataset Information

Automated Segmentation Errors When Using Optical Coherence Tomography to Measure Retinal Nerve Fiber Layer Thickness in Glaucoma.

ABSTRACT: To 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.Cross-sectional study.This 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.Automated 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.Automated 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.

SUBMITTER: Mansberger SL

PROVIDER: S-EPMC5548380 | biostudies-other | 2017 Feb

REPOSITORIES: biostudies-other

ACCESS DATA

Similar Datasets

Project description: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.PurposeTo 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.

Dataset Information

Automated Segmentation Errors When Using Optical Coherence Tomography to Measure Retinal Nerve Fiber Layer Thickness in Glaucoma.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure