July 2020
Volume 61, Issue 9
Open Access
ARVO Imaging in the Eye Conference Abstract  |   July 2020
Correlation of retinal thickness measures and optic nerve head parameters between different OCT scan types
Author Affiliations & Notes
  • Enrico Pellegrini
    Optos plc, Dunfermline, United Kingdom
  • Alan Fleming
    Optos plc, Dunfermline, United Kingdom
  • Michael Sinai
    Optos Inc, Marlborough, Massachusetts, United States
  • Nikolas London
    Retina Consultants San Diego, California, United States
  • Jano van Hemert
    Optos plc, Dunfermline, United Kingdom
  • Footnotes
    Commercial Relationships   Enrico Pellegrini, Optos plc (E); Alan Fleming, Optos plc (E); Michael Sinai, Optos Inc (E); Nikolas London, None; Jano van Hemert, Optos plc (E)
  • Footnotes
    Support  None
Investigative Ophthalmology & Visual Science July 2020, Vol.61, PB00102. doi:
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    • Get Citation

      Enrico Pellegrini, Alan Fleming, Michael Sinai, Nikolas London, Jano van Hemert; Correlation of retinal thickness measures and optic nerve head parameters between different OCT scan types. Invest. Ophthalmol. Vis. Sci. 2020;61(9):PB00102.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose : Retinal imaging devices on the market can produce optical coherence tomography (OCT) scan patterns with different fields of view and resolution to depict different regions of the retina. It is important that quantitative measures of the same structure, such as macular and peripapillary thickness values or optic nerve head (ONH) parameters, remain consistent regardless of the scan pattern that they are measured from. We have retrospectively analyzed the correlation between retinal measures obtained by automated processing of 3 different types of OCT scans.

Methods : Retina (9mm x 9mm, fovea-centered), ONH (6mm x 6mm, ONH-centered) and raster (14mm x 9mm, including both macular and ONH regions) scans (Fig.1) were acquired from a new multi-mode imaging device that combines ultra-widefield imaging and swept-source OCT (Silverstone, Optos Plc, Scotland). In total, 19 retina/raster and 31 ONH/raster scan pairs were available from a population of healthy and diseased eyes (AMD, DR, macula hole). A deep learning model was used for layer segmentation. Total retina thickness (TRT), nerve fiber layer (NFL) and ganglion cell complex (GCC) macular values, four quadrants of peripapillary NFL thickness and three ONH parameters (disc, cup and neuroretinal rim areas) were automatically derived and analyzed. Pearson’s correlation coefficients were calculated for corresponding pairs of measures. Bonferroni correction for multiple testing was applied to assess statistical significance.

Results : Significant correlation between all 10 investigated measures was observed (Fig.2). Values were especially high for macular thickness (all three above0.95). The same was also true for TRT in all 9 ETDRS subfields (i.e. 984, 0.983, 0.989, 0.988, 0.987, 0.885, 0.988, 0.955, 0.976).

Conclusions : Our results suggest measurements of the same retinal structures are consistent between scan patterns. This is important clinically as it shows patients can be followed accurately if scan patterns are changed over time. They also suggest that a single raster can be used in place of separately captured retina and ONH scans.

This is a 2020 Imaging in the Eye Conference abstract.

 

Examples of OCT scans: retina (left), ONH (middle), raster (right).

Examples of OCT scans: retina (left), ONH (middle), raster (right).

 

Parameters as mean (standard deviation), root mean square errors (RMSE) and Pearson’s r correlation coefficients between pairs of scans.

Parameters as mean (standard deviation), root mean square errors (RMSE) and Pearson’s r correlation coefficients between pairs of scans.

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