ArticlePloS one2014
Technical factors influencing cone packing density estimates in adaptive optics flood illuminated retinal images.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed, 33 citations in OpenAlex.
- The effect of sampling window size on topographical maps of foveal cone density.Frontiers in ophthalmology · 2024Article
- Adaptive Optics Imaging of Inherited Retinal Disease.Cold Spring Harbor perspectives in medicine · 2023Review
- Intergrader agreement of foveal cone topography measured using adaptive optics scanning light ophthalmoscopy.Biomedical optics express · 2022Article
- Investigating Biomarkers forInternational journal of molecular sciences · 2022Article
- Impact of Reference Center Choice on Adaptive Optics Imaging Cone Mosaic Analysis.Investigative ophthalmology & visual science · 2022Article
- Theoretical versus empirical measures of retinal magnification for scaling AOSLO images.Journal of the Optical Society of America. A, Optics, image science, and vision · 2021Article
- Promises and pitfalls of evaluating photoreceptor-based retinal disease with adaptive optics scanning light ophthalmoscopy (AOSLO).Progress in retinal and eye research · 2021Review
- Target Eccentricity and Form Influences Disparity Vergence Eye Movements Responses: A Temporal and Dynamic Analysis.Journal of eye movement research · 2019Article
- RAC-CNN: multimodal deep learning based automatic detection and classification of rod and cone photoreceptors in adaptive optics scanning light ophthalmoscope images.Biomedical optics express · 2019Article
- Adaptive optics imaging of the human retina.Progress in retinal and eye research · 2019Review
- Deep learning based detection of cone photoreceptors with multimodal adaptive optics scanning light ophthalmoscope images of achromatopsia.Biomedical optics express · 2018Article
- Distribution of cone density, spacing and arrangement in adult healthy retinas with adaptive optics flood illumination.PloS one · 2018Article
- Agreement in Cone Density Derived from Gaze-Directed Single Images Versus Wide-Field Montage Using Adaptive Optics Flood Illumination Ophthalmoscopy.Translational vision science & technology · 2017Article
- Open source software for automatic detection of cone photoreceptors in adaptive optics ophthalmoscopy using convolutional neural networks.Scientific reports · 2017Article
- Photoreceptor-Based Biomarkers in AOSLO Retinal Imaging.Investigative ophthalmology & visual science · 2017Review
- Understanding the changes of cone reflectance in adaptive optics flood illumination retinal images over three years.Biomedical optics express · 2016Article
- Automatic detection of cone photoreceptors in split detector adaptive optics scanning light ophthalmoscope images.Biomedical optics express · 2016Article
- Effects of Intraframe Distortion on Measures of Cone Mosaic Geometry from Adaptive Optics Scanning Light Ophthalmoscopy.Translational vision science & technology · 2016Article
- Methods for investigating the local spatial anisotropy and the preferred orientation of cones in adaptive optics retinal images.Visual neuroscience · 2016Article
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Authors and funding
3 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo investigate the influence of various technical factors on the variation of cone packing density estimates in adaptive optics flood illuminated retinal images.
methodsAdaptive optics images of the photoreceptor mosaic were obtained in fifteen healthy subjects. The cone density and Voronoi diagrams were assessed in sampling windows of 320×320 µm, 160×160 µm and 64×64 µm at 1.5 degree temporal and superior eccentricity from the preferred locus of fixation (PRL). The technical factors that have been analyzed included the sampling window size, the corrected retinal magnification factor (RMFcorr), the conversion from radial to linear distance from the PRL, the displacement between the PRL and foveal center and the manual checking of cone identification algorithm. Bland-Altman analysis was used to assess the agreement between cone density estimated within the different sampling window conditions.
resultsThe cone density declined with decreasing sampling area and data between areas of different size showed low agreement. A high agreement was found between sampling areas of the same size when comparing density calculated with or without using individual RMFcorr. The agreement between cone density measured at radial and linear distances from the PRL and between data referred to the PRL or the foveal center was moderate. The percentage of Voronoi tiles with hexagonal packing arrangement was comparable between sampling areas of different size. The boundary effect, presence of any retinal vessels, and the manual selection of cones missed by the automated identification algorithm were identified as the factors influencing variation of cone packing arrangements in Voronoi diagrams.
conclusionsThe sampling window size is the main technical factor that influences variation of cone density. Clear identification of each cone in the image and the use of a large buffer zone are necessary to minimize factors influencing variation of Voronoi diagrams of the cone mosaic.
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