June 2015
Volume 56, Issue 7
Free
ARVO Annual Meeting Abstract  |   June 2015
Measurement of retinal thickness in optical coherence tomography images in healthy and unhealthy retinas using a computerized algorithm
Author Affiliations & Notes
  • David Nguyen-Tri
    École d'optométrie, Université de Montréal, Montreal, QC, Canada
  • Jocelyn Faubert
    École d'optométrie, Université de Montréal, Montreal, QC, Canada
  • William Seiple
    Lighthouse Guild, New York, NY
    Schoold of Medicine, New York University, New York, NY
  • Olga Overbury
    École d'optométrie, Université de Montréal, Montreal, QC, Canada
  • Footnotes
    Commercial Relationships David Nguyen-Tri, None; Jocelyn Faubert, None; William Seiple, None; Olga Overbury, None
  • Footnotes
    Support None
Investigative Ophthalmology & Visual Science June 2015, Vol.56, 5282. doi:
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      David Nguyen-Tri, Jocelyn Faubert, William Seiple, Olga Overbury; Measurement of retinal thickness in optical coherence tomography images in healthy and unhealthy retinas using a computerized algorithm. Invest. Ophthalmol. Vis. Sci. 2015;56(7 ):5282.

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

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Abstract
 
Purpose
 

Single optical coherence tomography (OCT) images are generally noisy and it is difficult for an algorithm to rapidly and accurately establish the boundary of anatomical landmarks in cross-sectional images of the retina. Here, we present an image analysis algorithm that allows the delimitation of anatomical structures within the retina in OCT raster scan images and measurement of their relative distance in healthy and unhealthy retinas.

 
Methods
 

OCT raster scan images were obtained with an Optos OCT/SLO in healthy and in diseased eyes. A software algorithm was developed in order to process the images and delineate the vitreoretinal boundary and the retinal pigment epithelium. The distance between these boundaries was then measured in order to determine retinal thickness.

 
Results
 

The results show that the algorithm can reliably provide an accurate estimate of anatomical boundaries in OCT/SLO images. The results also demonstrate a good fit between the estimated location of the landmarks and their actual location as established by a human observer in both healthy and diseased retinas. In raster scans, individual thickness scans can be used to interpolate the position of retinal landmarks between scan lines and obtain a more detailed estimate of the position of retinal landmarks. These extrapolated boundary positions can be used to estimate retinal thickness and volume.

 
Conclusions
 

The algorithm provides a robust and valid method for delimiting the boundaries of retinal anatomical structures in both healthy and pathological eyes. This can be used to establish retinal thickness an measure volume in healthy and diseased eyes.  

 
OCT/SLO raster scan image with location of retinal landmarks as estimated by the computerized algorithm. The red line represents the estimated location of the vitreoretinal interface. The blue line represents the estimated location of the RPE.
 
OCT/SLO raster scan image with location of retinal landmarks as estimated by the computerized algorithm. The red line represents the estimated location of the vitreoretinal interface. The blue line represents the estimated location of the RPE.

 
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