Automated Segmentation Errors When Using Optical Coherence Tomography to Measure Retinal Nerve Fiber Layer Thickness in Glaucoma.
Mansberger Steven L, Menda Shivali A, Fortune Brad A, Gardiner Stuart K, Demirel Shaban
AI Summary
Automated OCT RNFL segmentation often underestimates thickness and overestimates glaucoma classification, especially in thinner RNFLs. Clinicians should manually refine segmentations for accurate glaucoma management.
Abstract
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.
MeSH Terms
Shields Classification
Key Concepts5
Automated segmentation of retinal nerve fiber layer (RNFL) thickness measurements using spectral domain OCT (Spectralis) resulted in a thinner global RNFL thickness (1.6 μm thinner, P < .001) when compared to manual refinement in a cross-sectional study of 3490 scans from 412 eyes of 213 individuals with glaucoma or glaucoma suspect.
When adjusted by operator, a multivariate model showed increased differences in retinal nerve fiber layer (RNFL) thickness measurements between automated segmentation and manual refinement with decreasing RNFL thickness (P < .001), decreasing scan quality (P < .001), and increasing age (P < .03) in a cross-sectional study of 3490 scans from 412 eyes of 213 individuals with glaucoma or glaucoma suspect.
Manual refinement of optical coherence tomography (OCT) retinal layer segmentation 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, in a cross-sectional study of 3490 scans from 412 eyes of 213 individuals with glaucoma or glaucoma suspect.
Automated segmentation without manual refinement of optical coherence tomography (OCT) retinal nerve fiber layer (RNFL) thickness measurements overestimated the classification of glaucoma in a cross-sectional study of 3490 scans from 412 eyes of 213 individuals with glaucoma or glaucoma suspect.
Operators should inspect and manually refine optical coherence tomography (OCT) retinal layer segmentation when assessing retinal nerve fiber layer (RNFL) thickness in the management of patients with glaucoma, based on findings from a cross-sectional study of 3490 scans from 412 eyes of 213 individuals with glaucoma or glaucoma suspect.
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