Robert F Cooper1,2, Geoffrey K Aguirre3, Jessica I W Morgan1,4. 1. Scheie Eye Institute, Department of Ophthalmology, University of Pennsylvania, Philadelphia, PA, USA. 2. Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA. 3. Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA. 4. Center for Advanced Retinal and Ocular Therapeutics, University of Pennsylvania, Philadelphia, PA, USA.
Abstract
PURPOSE: To introduce and validate a novel, fully automated algorithm for determining pointwise intercell distance (ICD) and cone density. METHODS: We obtained images of the photoreceptor mosaic from 14 eyes of nine subjects without retinal pathology at two time points using an adaptive optics scanning laser ophthalmoscope. To automatically determine ICD, the radial average of the discrete Fourier transform (DFT) of the image was analyzed using a multiscale, fit-based algorithm to find the modal spacing. We then converted the modal spacing to ICD by assuming a hexagonally packed mosaic. The reproducibility of the algorithm was assessed between the two datasets, and accuracy was evaluated by comparing the results against those calculated from manually identified cones. Finally, the algorithm was extended to determine pointwise ICD and density in montages by calculating modal spacing over an overlapping grid of regions of interest (ROIs). RESULTS: The differences of DFT-derived ICD between the test and validation datasets were 3.2% ± 3.5% (mean ± SD), consistent with the differences in directly calculated ICD (1.9% ± 2.9%). The average ICD derived by the automated method was not significantly different between the development and validation datasets and was equivalent to the directly calculated ICD. When applied to a full montage, the automated algorithm produced estimates of cone density across retinal eccentricity that well match prior empiric measurements. CONCLUSIONS: We created an accurate, repeatable, and fully automated algorithm for determining ICD and density in both individual ROIs and across entire montages. TRANSLATIONAL RELEVANCE: The use of fully automated and validated algorithms will enable rapid analysis over the full photoreceptor montage. Copyright 2019 The Authors.
PURPOSE: To introduce and validate a novel, fully automated algorithm for determining pointwise intercell distance (ICD) and cone density. METHODS: We obtained images of the photoreceptor mosaic from 14 eyes of nine subjects without retinal pathology at two time points using an adaptive optics scanning laser ophthalmoscope. To automatically determine ICD, the radial average of the discrete Fourier transform (DFT) of the image was analyzed using a multiscale, fit-based algorithm to find the modal spacing. We then converted the modal spacing to ICD by assuming a hexagonally packed mosaic. The reproducibility of the algorithm was assessed between the two datasets, and accuracy was evaluated by comparing the results against those calculated from manually identified cones. Finally, the algorithm was extended to determine pointwise ICD and density in montages by calculating modal spacing over an overlapping grid of regions of interest (ROIs). RESULTS: The differences of DFT-derived ICD between the test and validation datasets were 3.2% ± 3.5% (mean ± SD), consistent with the differences in directly calculated ICD (1.9% ± 2.9%). The average ICD derived by the automated method was not significantly different between the development and validation datasets and was equivalent to the directly calculated ICD. When applied to a full montage, the automated algorithm produced estimates of cone density across retinal eccentricity that well match prior empiric measurements. CONCLUSIONS: We created an accurate, repeatable, and fully automated algorithm for determining ICD and density in both individual ROIs and across entire montages. TRANSLATIONAL RELEVANCE: The use of fully automated and validated algorithms will enable rapid analysis over the full photoreceptor montage. Copyright 2019 The Authors.
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