Normative Data and Minimally Detectable Change for Inner Retinal Layer Thicknesses Using a Semi-automated OCT Image Segmentation Pipeline

Seyedamirhosein Motamedi; Kay Gawlik; Noah Ayadi; Hanna G Zimmermann; Susanna Asseyer; Charlotte Bereuter; Janine Mikolajczak; Friedemann Paul; Ella Maria Kadas; Alexander Ulrich Brandt

doi:10.3389/fneur.2019.01117

Normative Data and Minimally Detectable Change for Inner Retinal Layer Thicknesses Using a Semi-automated OCT Image Segmentation Pipeline

Front Neurol. 2019 Nov 25:10:1117. doi: 10.3389/fneur.2019.01117. eCollection 2019.

Authors

Affiliations

¹ NeuroCure Clinical Research Center, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany.
² Experimental and Clinical Research Center, Max Delbrück Center for Molecular Medicine and Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany.
³ Department of Neurology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany.
⁴ Department of Neurology, University of California, Irvine, Irvine, CA, United States.

Abstract

Neurodegenerative and neuroinflammatory diseases regularly cause optic nerve and retinal damage. Evaluating retinal changes using optical coherence tomography (OCT) in diseases like multiple sclerosis has thus become increasingly relevant. However, intraretinal segmentation, a necessary step for interpreting retinal changes in the context of these diseases, is not standardized and often requires manual correction. Here we present a semi-automatic intraretinal layer segmentation pipeline and establish normative values for retinal layer thicknesses at the macula, including dependencies on age, sex, and refractive error. Spectral domain OCT macular 3D volume scans were obtained from healthy participants using a Heidelberg Engineering Spectralis OCT. A semi-automated segmentation tool (SAMIRIX) based on an interchangeable third-party segmentation algorithm was developed and employed for segmentation, correction, and thickness computation of intraretinal layers. Normative data is reported from a 6 mm Early Treatment Diabetic Retinopathy Study (ETDRS) circle around the fovea. An interactive toolbox for the normative database allows surveying for additional normative data. We cross-sectionally evaluated data from 218 healthy volunteers (144 females/74 males, age 36.5 ± 12.3 years, range 18-69 years). Average macular thickness (MT) was 313.70 ± 12.02 μm, macular retinal nerve fiber layer thickness (mRNFL) 39.53 ± 3.57 μm, ganglion cell and inner plexiform layer thickness (GCIPL) 70.81 ± 4.87 μm, and inner nuclear layer thickness (INL) 35.93 ± 2.34 μm. All retinal layer thicknesses decreased with age. MT and GCIPL were associated with sex, with males showing higher thicknesses. Layer thicknesses were also positively associated with each other. Repeated-measurement reliability for the manual correction of automatic intraretinal segmentation results was excellent, with an intra-class correlation coefficient >0.99 for all layers. The SAMIRIX toolbox can simplify intraretinal segmentation in research applications, and the normative data application may serve as an expandable reference for studies, in which normative data cannot be otherwise obtained.

Keywords: healthy population; inner retinal layer; macula; minimally detectable change; normative data; optical coherence tomography (OCT); retina; segmentation.