In vivo assessment of retinal neuronal layers in multiple sclerosis with manual and automated optical coherence tomography segmentation techniques.

Macular optical coherence tomography (OCT) segmentation, enabling quantification of retinal axonal and neuronal subpopulations, may help elucidate the neuroretinal pathobiology of multiple sclerosis (MS). This study aimed to determine the agreement, reproducibility, and visual correlations of retinal layer thicknesses measured by different OCT segmentation techniques, on two spectral-domain OCT devices. Macular scans of 52 MS patients and 30 healthy controls from Spectralis OCT and Cirrus HD-OCT were segmented using fully manual (Spectralis), computer-aided manual (Spectralis and Cirrus), and fully automated (Cirrus) segmentation techniques. Letter acuity was recorded. Bland-Altman analyses revealed low mean differences across OCT segmentation techniques on both devices for ganglion cell + inner plexiform layers (GCIP; 0.76-2.43 μm), inner nuclear + outer plexiform layers (INL + OPL; 0.36-1.04 μm), and outer nuclear layers including photoreceptor segment (ONL + PR; 1.29-3.52 μm) thicknesses. Limits of agreement for GCIP and ONL + PR thicknesses were narrow. Results of fully manual and computer-aided manual segmentation were comparable to those of fully automated segmentation. MS patients demonstrated macular RNFL, GCIP, and ONL + PR thinning compared to healthy controls across OCT segmentation techniques, irrespective of device (p < 0.03 for all). Low-contrast letter acuity in MS correlated significantly and more strongly with GCIP than peripapillary RNFL thicknesses, regardless of the segmentation method or device. GCIP and ONL + PR thicknesses, measured by different OCT devices and segmentation techniques, are reproducible and agree at the individual and cohort levels. GCIP thinning in MS correlates with visual dysfunction. Significant ONL + PR thinning, detectable across OCT segmentation techniques and devices, strongly supports ONL pathology in MS. Fully automated, fully manual and computer-assisted manual OCT segmentation techniques compare closely, highlighting the utility of accurate and time-efficient automated segmentation outcomes in MS clinical trials.