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Neurol Neuroimmunol Neuroinflamm. 2018 Mar 13;5(3):e449. doi: 10.1212/NXI.0000000000000449. eCollection 2018 May.

Multicenter reliability of semiautomatic retinal layer segmentation using OCT.

Author information

NeuroCure Clinical Research Center (T.O., F.P., A.U.B.), Charité-Universitätsmedizin Berlin, Germany; Department of Ophthalmology (G.L.T.), University Hospital Zurich, University of Zurich; Neuroimmunology and Multiple Sclerosis Research Section (S.L., S.S.), Department of Neurology, University Hospital Zurich, University of Zurich, Switzerland; Center of Neuroimmunology (I.G., P.V.), Institut d'Investigacions Biomediques August Pi Sunyer (IDIBAPS)-Hospital Clinic, Barcelona, Spain; Division of Neuroinflammation and Glial Biology (R.N., C.S., A.J.G.), Department of Neurology, University of California San Francisco; Neuro-ophthalmology Division (A.J.G.), Department of Ophthalmology, University of California, San Francisco; Multiple Sclerosis Center (L.B., A.P.), Departments of Neurology and Ophthalmology, Neuro-ophthalmology Expertise Centre, VUmc, Amsterdam and Moorfields Eye Hospital (A.P.), The National Hospital for Neurology and Neurosurgery and UCL, United Kingdom; Clinical and Experimental Multiple Sclerosis Research Center (F.P.), Department of Neurology, Charité-Universitätsmedizin Berlin; Experimental and Clinical Research Center (F.P., A.U.B.), Charité-Universitätsmedizin Berlin and Max-Delbrück Center for Molecular Medicine, Germany; Department of Methods and Experimental Psychology (I.G.), Faculty of Psychology and Education, Universidad de Deusto, Bilbao, Spain.



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.

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