Cynomolgus monkey’s choroid reference database derived from hybrid deep learning optical coherence tomography segmentation

Abstract

Cynomolgus monkeys exhibit human-like features, such as a fovea, so they are often used in non-clinical research. Nevertheless, little is known about the natural variation of the choroidal thickness in relation to origin and sex. A combination of deep learning and a deterministic computer vision algorithm was applied for automatic segmentation of foveolar optical coherence tomography images in cynomolgus monkeys. The main evaluation parameters were choroidal thickness and surface area directed from the deepest point on OCT images within the fovea, marked as the nulla with regard to sex and origin. Reference choroid landmarks were set underneath the nulla and at 500 µm intervals laterally up to a distance of 2000 µm nasally and temporally, complemented by a sub-analysis of the central bouquet of cones. 203 animals contributed 374 eyes for a reference choroid database. The overall average central choroidal thickness was 193 µm with a coefficient of variation of 7.8%, and the overall mean surface area of the central bouquet temporally was 19,335 µm2 and nasally was 19,283 µm2. The choroidal thickness of the fovea appears relatively homogeneous between the sexes and the studied origins. However, considerable natural variation has been observed, which needs to be appreciated.

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“… 41 The algorithm could also segment the choroid, although measurement of central retina thickness was the main purpose of this study. 41 The similarity between the current study and the method proposed by Maloca et al 41 , 57 is that both studies used the nulla as a reference landmark. Following the path paved by Maloca et al, our group utilized the nulla to determine the choroidal ROI within a certain width for longitudinal tracking of microscopic changes.…”

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“…Deep learning has been successfully applied to optical coherence tomography (OCT) in various studies. Maloca et al (2022) developed a reference database for the choroid of Cynomolgus monkeys using hybrid deep learning segmentation. Another study Maloca et al (2023) investigated the impact of ground truth data size and human graders on DL algorithms for OCT segmentation, revealing a linear relationship between ground truth ambiguity and performance.…”

“…Another study Maloca et al (2023) investigated the impact of ground truth data size and human graders on DL algorithms for OCT segmentation, revealing a linear relationship between ground truth ambiguity and performance. Denk et al (2023) created a reference database for the retina of Cynomolgus monkeys using hybrid deep learning segmentation. Allegrini et al (2023) examined the effect of optical degradation from cataract using a new deep learning segmentation algorithm.…”

“…Deep learning has been successfully applied to optical coherence tomography (OCT) in various studies Maloca et al (2022). developed a reference database for the choroid of Cynomolgus monkeys using hybrid deep learning segmentation.…”

“…developed a reference database for the choroid of Cynomolgus monkeys using hybrid deep learning segmentation. Another studyMaloca et al (2023) investigated the impact of ground truth data size and human graders on DL algorithms for OCT segmentation, revealing a linear relationship between ground truth ambiguity and performance Denk et al (2023). created a reference database for the retina of Cynomolgus monkeys using hybrid deep learning segmentation Allegrini et al (2023).…”