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Error correction and quantitative subanalysis of optical coherence tomography data using computer-assisted grading.

Srinivas R Sadda1, Sandra Joeres, Ziqiang Wu

  • 1Doheny Image Reading Center, Doheny Eye Institute, Keck School of Medicine of the University of Southern California, Los Angeles, California 90033, USA. sadda@usc.edu

Investigative Ophthalmology & Visual Science
|January 26, 2007
PubMed
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Computer-assisted grading of optical coherence tomography (OCT) data allows for accurate retinal thickness measurements and feature subanalysis. This method shows consistency with automated analysis and aids in error correction for improved OCT data interpretation.

Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Biomedical Engineering

Background:

  • Optical coherence tomography (OCT) is crucial for retinal imaging.
  • Automated analysis of OCT data can present errors in retinal boundary detection.
  • Accurate quantitative measurements are essential for diagnosing and monitoring retinal diseases.

Purpose of the Study:

  • To demonstrate the utility of computer-assisted grading for feature subanalysis of OCT data.
  • To evaluate the accuracy and consistency of manual retinal border identification using custom software.
  • To show the potential for error correction in automated OCT thickness measurements.

Main Methods:

  • Custom software (OCTOR) was developed for manual definition of retinal borders on StratusOCT scans.

Related Experiment Videos

  • Retinal thickness, foveal center point (FCP), and macular volume were measured.
  • Comparisons were made between two human graders and automated Stratus analysis using Bland-Altman plots and Pearson correlation.

    • Main Results:

    • Manual OCT grading showed high correlation (R(2) >= 0.98) with automated Stratus analysis for retinal thickness and FCP.
    • Mean differences in thickness measurements between OCTOR graders and between OCTOR and Stratus were minimal (1.7-2.3 microm).
    • Volume measurements also showed non-significant differences between the methods.

    Conclusions:

    • Manual identification of retinal boundaries in OCT scans yields measurements consistent with automated analysis.
    • Computer-assisted OCT grading can correct errors in automated boundary detection.
    • This approach is valuable for quantitative subanalysis of specific retinal features, such as subretinal fluid or pigment epithelial detachment.