Optical Coherence Tomography Segmentation Errors of the Retinal Nerve Fiber Layer Persist Over Time

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Précis:There are errors in automated segmentation of the retinal nerve fiber layer 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 retinal nerve fiber layer (RNFL) thickness measurements persist longitudinally.Methods:This was a cohort study. We used spectral domain OCT (Spectralis, Heidelberg Engineering, Heidelberg, Germany) 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.Results:We 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 standard deviation of residuals from the longitudinal trend line when compared to manual refinement (1.56▒μm 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.Conclusions:Errors 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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