Volumetric subfield analysis of cynomolgus monkey's choroid derived from hybrid machine learning optical coherence tomography segmentation

PLoS One. 2022 Sep 23;17(9):e0275050. doi: 10.1371/journal.pone.0275050. eCollection 2022.

Abstract

This study aimed to provide volumetric choroidal readings regarding sex, origin, and eye side from healthy cynomolgus monkey eyes as a reference database using optical coherence tomography (OCT) imaging. A machine learning (ML) algorithm was used to extract the choroid from the volumetric OCT data. Classical computer vision methods were then applied to automatically identify the deepest location in the foveolar depression. The choroidal thickness was determined from this reference point. A total of 374 eyes of 203 cynomolgus macaques from Asian and Mauritius origin were included in the analysis. The overall subfoveolar mean choroidal volume in zone 1, in the region of the central bouquet, was 0.156 mm3 (range, 0.131-0.193 mm3). For the central choroid volume, the coefficient of variation (CV) was found of 6.3%, indicating relatively little variation. Our results show, based on analyses of variance, that monkey origin (Asian or Mauritius) does not influence choroid volumes. Sex had a significant influence on choroidal volumes in the superior-inferior axis (p ≤ 0.01), but not in the fovea centralis. A homogeneous foveolar choroidal architecture was also observed.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Animals
  • Choroid* / diagnostic imaging
  • Macaca fascicularis
  • Machine Learning
  • Tomography, Optical Coherence* / methods

Grants and funding

This study was supported by Hoffmann–La Roche Ltd. (Basel, Switzerland). The funder provided support in the form of salaries for TH, MJ, and ND, as well as PMM and PH (consultants). The finder did not play any role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.