Automated identification of cone photoreceptors in adaptive optics retinal images
- PMID: 17429481
- DOI: 10.1364/josaa.24.001358
Automated identification of cone photoreceptors in adaptive optics retinal images
Abstract
In making noninvasive measurements of the human cone mosaic, the task of labeling each individual cone is unavoidable. Manual labeling is a time-consuming process, setting the motivation for the development of an automated method. An automated algorithm for labeling cones in adaptive optics (AO) retinal images is implemented and tested on real data. The optical fiber properties of cones aided the design of the algorithm. Out of 2153 manually labeled cones from six different images, the automated method correctly identified 94.1% of them. The agreement between the automated and the manual labeling methods varied from 92.7% to 96.2% across the six images. Results between the two methods disagreed for 1.2% to 9.1% of the cones. Voronoi analysis of large montages of AO retinal images confirmed the general hexagonal-packing structure of retinal cones as well as the general cone density variability across portions of the retina. The consistency of our measurements demonstrates the reliability and practicality of having an automated solution to this problem.
Similar articles
-
Automated identification of cone photoreceptors in adaptive optics optical coherence tomography images using transfer learning.Biomed Opt Express. 2018 Oct 10;9(11):5353-5367. doi: 10.1364/BOE.9.005353. eCollection 2018 Nov 1. Biomed Opt Express. 2018. PMID: 30460133 Free PMC article.
-
Automatic extraction of retinal features from colour retinal images for glaucoma diagnosis: a review.Comput Med Imaging Graph. 2013 Oct-Dec;37(7-8):581-96. doi: 10.1016/j.compmedimag.2013.09.005. Epub 2013 Sep 27. Comput Med Imaging Graph. 2013. PMID: 24139134 Review.
-
Adaptive-optics imaging of human cone photoreceptor distribution.J Opt Soc Am A Opt Image Sci Vis. 2008 Dec;25(12):3021-9. doi: 10.1364/josaa.25.003021. J Opt Soc Am A Opt Image Sci Vis. 2008. PMID: 19037393 Free PMC article.
-
Automated analysis of differential interference contrast microscopy images of the foveal cone mosaic.J Opt Soc Am A Opt Image Sci Vis. 2008 May;25(5):1181-9. doi: 10.1364/josaa.25.001181. J Opt Soc Am A Opt Image Sci Vis. 2008. PMID: 18451927
-
Photoreceptor counting and montaging of en-face retinal images from an adaptive optics fundus camera.J Opt Soc Am A Opt Image Sci Vis. 2007 May;24(5):1364-72. doi: 10.1364/josaa.24.001364. J Opt Soc Am A Opt Image Sci Vis. 2007. PMID: 17429482 Free PMC article.
Cited by
-
Alignment, calibration, and validation of an adaptive optics scanning laser ophthalmoscope for high-resolution human foveal imaging.Appl Opt. 2024 Jan 20;63(3):730-742. doi: 10.1364/AO.504283. Appl Opt. 2024. PMID: 38294386
-
Pearls and Pitfalls of Adaptive Optics Ophthalmoscopy in Inherited Retinal Diseases.Diagnostics (Basel). 2023 Jul 19;13(14):2413. doi: 10.3390/diagnostics13142413. Diagnostics (Basel). 2023. PMID: 37510157 Free PMC article. Review.
-
Modeling rod and cone photoreceptor cell survival in vivo using optical coherence tomography.Sci Rep. 2023 Apr 27;13(1):6896. doi: 10.1038/s41598-023-33694-y. Sci Rep. 2023. PMID: 37106000 Free PMC article.
-
Comprehensive automatic processing and analysis of adaptive optics flood illumination retinal images on healthy subjects.Biomed Opt Express. 2023 Jan 30;14(2):945-970. doi: 10.1364/BOE.471881. eCollection 2023 Feb 1. Biomed Opt Express. 2023. PMID: 36874506 Free PMC article.
-
Deep learning-enabled volumetric cone photoreceptor segmentation in adaptive optics optical coherence tomography images of normal and diseased eyes.Biomed Opt Express. 2023 Jan 23;14(2):815-833. doi: 10.1364/BOE.478693. eCollection 2023 Feb 1. Biomed Opt Express. 2023. PMID: 36874491 Free PMC article.
Publication types
MeSH terms
Grants and funding
LinkOut - more resources
Full Text Sources
Other Literature Sources