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Multicenter reliability of semiautomatic retinal layer segmentation using OCT

2018; Wolters Kluwer; Volume: 5; Issue: 3 Linguagem: Inglês

10.1212/nxi.0000000000000449

2332-7812

Timm Oberwahrenbrock, Ghislaine L. Traber, Sebastian Lukas, Iñigo Gabilondo, Rachel Nolan, Christopher Songster, Lisanne J. Balk, Axel Petzold, Friedemann Paul, Pablo Villoslada, Alexander U. Brandt, Ari Green, Sven Schippling,

Retinal and Optic Conditions

To evaluate the inter-rater reliability of semiautomated segmentation of spectral domain optical coherence tomography (OCT) macular volume scans.Macular OCT volume scans of left eyes from 17 subjects (8 patients with MS and 9 healthy controls) were automatically segmented by Heidelberg Eye Explorer (v1.9.3.0) beta-software (Spectralis Viewing Module v6.0.0.7), followed by manual correction by 5 experienced operators from 5 different academic centers. The mean thicknesses within a 6-mm area around the fovea were computed for the retinal nerve fiber layer, ganglion cell layer (GCL), inner plexiform layer (IPL), inner nuclear layer, outer plexiform layer (OPL), and outer nuclear layer (ONL). Intraclass correlation coefficients (ICCs) were calculated for mean layer thickness values. Spatial distribution of ICC values for the segmented volume scans was investigated using heat maps.Agreement between raters was good (ICC > 0.84) for all retinal layers, particularly inner retinal layers showed excellent agreement across raters (ICC > 0.96). Spatial distribution of ICC showed highest values in the perimacular area, whereas the ICCs were poorer for the foveola and the more peripheral macular area. The automated segmentation of the OPL and ONL required the most correction and showed the least agreement, whereas differences were less prominent for the remaining layers.Automated segmentation with manual correction of macular OCT scans is highly reliable when performed by experienced raters and can thus be applied in multicenter settings. Reliability can be improved by restricting analysis to the perimacular area and compound segmentation of GCL and IPL.