Maloca, Peter M;
Seeger, Christine;
Booler, Helen;
Valmaggia, Philippe;
Kawamoto, Ken;
Kaba, Qayim;
Inglin, Nadja;
... Denk, Nora; + view all
(2021)
Uncovering of intraspecies macular heterogeneity in cynomolgus monkeys using hybrid machine learning optical coherence tomography image segmentation.
Scientific Reports
, 11
(1)
, Article 20647. 10.1038/s41598-021-99704-z.
Preview |
PDF
Uncovering of intraspecies macular heterogeneity in cynomolgus monkeys using hybrid machine learning optical coherence tomog.pdf - Other Download (2MB) | Preview |
Abstract
The fovea is a depression in the center of the macula and is the site of the highest visual acuity. Optical coherence tomography (OCT) has contributed considerably in elucidating the pathologic changes in the fovea and is now being considered as an accompanying imaging method in drug development, such as antivascular endothelial growth factor and its safety profiling. Because animal numbers are limited in preclinical studies and automatized image evaluation tools have not yet been routinely employed, essential reference data describing the morphologic variations in macular thickness in laboratory cynomolgus monkeys are sparse to nonexistent. A hybrid machine learning algorithm was applied for automated OCT image processing and measurements of central retina thickness and surface area values. Morphological variations and the effects of sex and geographical origin were determined. Based on our findings, the fovea parameters are specific to the geographic origin. Despite morphological similarities among cynomolgus monkeys, considerable variations in the foveolar contour, even within the same species but from different geographic origins, were found. The results of the reference database show that not only the entire retinal thickness, but also the macular subfields, should be considered when designing preclinical studies and in the interpretation of foveal data.
| Type: | Article |
|---|---|
| Title: | Uncovering of intraspecies macular heterogeneity in cynomolgus monkeys using hybrid machine learning optical coherence tomography image segmentation |
| Location: | England |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1038/s41598-021-99704-z |
| Publisher version: | https://doi.org/10.1038/s41598-021-99704-z |
| Language: | English |
| Additional information: | © 2023 Springer Nature Limited. This article is licensed under a Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/). |
| Keywords: | Experimental models of disease, Medical research, Translational research |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Institute of Ophthalmology |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10165118 |
Archive Staff Only
 |
View Item |
