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Fully Automatic Software for Retinal Thickness in Eyes With Diabetic Macular Edema From Images Acquired by Cirrus and Spectralis Systems

Authors :
Joseph A. Izatt
Sina Farsiu
Joo-Yong Lee
Cynthia A. Toth
Stephanie J. Chiu
Glenn J. Jaffe
Pratul P. Srinivasan
Publication Year :
2013
Publisher :
The Association for Research in Vision and Ophthalmology, 2013.

Abstract

Purpose To determine whether a novel automatic segmentation program, the Duke Optical Coherence Tomography Retinal Analysis Program (DOCTRAP), can be applied to spectral-domain optical coherence tomography (SD-OCT) images obtained from different commercially available SD-OCT in eyes with diabetic macular edema (DME). Methods A novel segmentation framework was used to segment the retina, inner retinal pigment epithelium, and Bruch's membrane on images from eyes with DME acquired by one of two SD-OCT systems, Spectralis or Cirrus high definition (HD)-OCT. Thickness data obtained by the DOCTRAP software were compared with those produced by Spectralis and Cirrus. Measurement agreement and its dependence were assessed using intraclass correlation (ICC). Results A total of 40 SD-OCT scans from 20 subjects for each machine were included in the analysis. Spectralis: the mean thickness in the 1-mm central area determined by DOCTRAP and Spectralis was 463.8 ± 107.5 μm and 467.0 ± 108.1 μm, respectively (ICC, 0.999). There was also a high level agreement in surrounding areas (out to 3 mm). Cirrus: the mean thickness in the 1-mm central area was 440.8 ± 183.4 μm and 442.7 ± 182.4 μm by DOCTRAP and Cirrus, respectively (ICC, 0.999). The thickness agreement in surrounding areas (out to 3 mm) was more variable due to Cirrus segmentation errors in one subject (ICC, 0.734-0.999). After manual correction of the errors, there was a high level of thickness agreement in surrounding areas (ICC, 0.997-1.000). Conclusions The DOCTRAP may be useful to compare retinal thicknesses in eyes with DME across OCT platforms.

Details

Language :
English
Database :
OpenAIRE
Accession number :
edsair.doi.dedup.....5ca1f0d2dbc98a44d1891c7a2ef16eeb