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Comparison of the Iowa Reference Algorithm to the Heidelberg Spectralis optical coherence tomography segmentation

Jasmine Q Sun1, Brendan McGeehan1, Kim Firn2

  • 1Department of Ophthalmology, Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.

Journal of Biophotonics
|February 15, 2020
PubMed
Summary

Comparing spectral-domain optical coherence tomography segmentation algorithms reveals minor differences in retinal layer thickness measurements. Manual correction minimally impacts results, aiding neurodegeneration research.

Keywords:
Iowa Reference AlgorithmSpectralisfrontotemporal degenerationoptical coherence tomographyretinal layer thicknesssegmentation algorithms

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Area of Science:

  • Ophthalmology
  • Neuroscience
  • Medical Imaging

Background:

  • Accurate retinal layer thickness measurement is crucial for studying neurodegenerative diseases using spectral-domain optical coherence tomography (SD-OCT).
  • Segmentation algorithms can introduce variability in measurements and errors, necessitating an understanding of their performance and the impact of manual corrections.

Purpose of the Study:

  • To compare retinal layer thickness measurements between two common SD-OCT segmentation algorithms: the Iowa Reference Algorithm and Heidelberg Spectralis.
  • To identify locations of segmentation errors and assess the impact of manual correction on thickness measurements in patients with frontotemporal degeneration.

Main Methods:

  • Macular SD-OCT images from frontotemporal degeneration patients and controls were analyzed.
  • Retinal layer thickness measurements from the Iowa Reference Algorithm and Heidelberg Spectralis were compared.
  • Manual correction of significant segmentation errors was performed and its impact evaluated.

Main Results:

  • Small differences were observed in most retinal layer thickness measurements between the two algorithms.
  • Outer sectors of the Early Treatment Diabetic Retinopathy Study grid showed a higher percentage of eyes requiring correction compared to inner sectors for the retinal nerve fiber layer (RNFL).
  • Manual corrections had a mild effect, causing at most a 5% change in RNFL thickness.

Conclusions:

  • Researchers can be informed on the optimal use of different segmentation algorithms for comparing retinal layer thicknesses in neurodegeneration studies.
  • Understanding algorithm-specific errors and the effect of manual corrections enhances the reliability of SD-OCT in clinical research.