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

https://doi.org/10.1016/j.ajo.2016.10.020Get rights and content

Purpose

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.

Design

Cross-sectional study.

Methods

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.

Results

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.

Conclusions

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.

Section snippets

Methods

We included data from participants enrolled in the ongoing Portland Progression Project11, 12, 13, 14 at Legacy Devers Eye Institute, Portland, Oregon. The Legacy Health Institutional Review Board approved this study. All participants provided their consent after they were informed about the risks and benefits of their participation. The study adhered to the tenets of the Declaration of Helsinki.

The Portland Progression Project includes participants with a diagnosis of glaucoma suspect or open

Results

This study included 3490 scans from 412 eyes (209 right and 203 left eyes) of 213 individuals. Four scans (0.1%) did not include complete measurements in clock hour 9 (temporal sector) and were not included in the evaluation of this sector or in the changes in glaucoma classification. Table 1 describes the participants. They were mostly white, 58% female, early glaucoma by mean deviation of their visual field, and a mean (±SD) age of 66.7 ± 10.9 years.

Discussion

OCT provides objective and repeatable measurements of optic nerve head structure and RNFL thickness.19, 20, 21, 22 However, variability in RNFL thickness measurements and failure of RNFL segmentation algorithms can lead to over- or underestimations of RNFL thickness.10 We were interested in the magnitude, associations, and locations of errors associated with automated segmentation of the RNFL when operators do not use manual refinement. In this study, we used manual refinement of the automated

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