Purpose: This study assessed agreement between an automated spectral-domain optical coherence tomography (SD-OCT) retinal segmentation software and manually corrected segmentation to validate its use in a prospective clinical study of neurodegenerative diseases (NDD).
Methods: The sample comprised 30 subjects with NDD, including vascular cognitive impairment, frontotemporal dementia, Parkinson's disease, and Alzheimer's disease. Macular SD-OCT scans were acquired and segmented using Heidelberg Spectralis. For the central foveal B scan of each eye, eight segmentation lines were examined to determine the proportion of each line that the software erroneously delineated. Errors in four lines were manually corrected in all B scans spanning a 6-mm circle centered on the foveola. Mean volume and thickness measurements for four retinal layers (total retina, retinal nerve fiber layer [RNFL], inner retinal layers, and outer retinal layers) were obtained before and after correction.
Results: The outer plexiform layer line had one of the lowest mean error ratios (2%), while RNFL had the highest (23%). Agreement between automated software and trained observer was excellent (ICC > 0.98) for retinal thickness and volume of all layers. Mean volume differences between software and observers for the four layers ranged from -0.003 to 0.006 mm3. Mean thickness differences ranged from -1.855 to 1.859 μm.
Conclusions: Despite occasional small errors in software-generated retinal sublayer segmentation, agreement was excellent between software-derived and observer-corrected mean volume and thickness sublayer measurements.
Translational relevance: Automated SD-OCT segmentation software generates valid measurements of retinal layer volume and thickness in NDD subjects, thereby avoiding the need to manually correct nonobvious delineation errors.
Keywords: Parkinson's disease; automated segmentation; optical coherence tomography; retinal nerve fibre layer; retinal thickness.
Copyright 2019 The Authors.