Scientific Publications Database

Article Title: Validation of Optical Coherence Tomography Retinal Segmentation in Neurodegenerative Disease
Authors: Wong, Bryan M.; Cheng, Richard W.; Mandelcorn, Efrem D.; Margolin, Edward; El-Defrawy, Sherif; Yan, Peng; Santiago, Anna T.; Leontieva, Elena; Lou, Wendy; Hatch, Wendy; Hudso, Christopher; Bartha, Robert; Black, Sandra E.; Borrie, Michael; Corbett, Dale; Finger, Elizabeth; Freedman, Morris; Greenberg, Barry; Grimes, David A.; Hegele, Robert A.; Hudson, Christopher; Lang, Anthony E.; Masellis, Mario; McIlroy, William E.; McLaughlin, Paula M.; Montero-Odasso, Manuel; Munoz, David G.; Munoz, Douglas P.; Orange, J. B.; Strong, Michael J.; Strother, Stephen C.; Swartz, Richard H.; Symons, Sean; Tartaglia, Maria Carmela; Troyer, Angela; Zinman, Lorne
Journal: TRANSLATIONAL VISION SCIENCE & TECHNOLOGY Volume 8 Issue 5
Date of Publication:2019
Abstract:
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 mm(3). Mean thickness differences ranged from -1.855 to 1.859 mu m.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.