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. 2023 Apr 9;13(1):5797.
doi: 10.1038/s41598-023-32739-6.

Cynomolgus monkey's retina volume reference database based on hybrid deep learning optical coherence tomography segmentation

Nora Denk  1   2 , Christian Freichel  1 , Philippe Valmaggia  2   3 , Nadja Inglin  3 , Hendrik P N Scholl  2   3 , Pascal Kaiser  4 , Sylvie Wise  5 , Marc Vezina  5 , Peter M Maloca  6   7   8
Affiliations

Cynomolgus monkey's retina volume reference database based on hybrid deep learning optical coherence tomography segmentation

Nora Denk et al. Sci Rep. .

Abstract

Cynomolgus monkeys (Macaca fascicularis) are commonly used in pre-clinical ocular studies. However, studies that report the morphological features of the macaque retina are based only on minimal sample sizes; therefore, little is known about the normal distribution and background variation. This study was conducted using optical coherence tomography (OCT) imaging to investigate the variations in retinal volumes of healthy cynomolgus monkeys and the effects of sex, origin, and eye side on the retinal volumes to establish a comprehensive reference database. A machine-learning algorithm was employed to segment the retina within the OCT data (i.e., generated pixel-wise labels). Furthermore, a classical computer vision algorithm has identified the deepest point in a foveolar depression. The retinal volumes were determined and analyzed based on this reference point and segmented retinal compartments. Notably, the overall foveolar mean volume in zone 1, which is the region of the sharpest vision, was 0.205 mm3 (range 0.154-0.268 mm3), with a relatively low coefficient of variation of 7.9%. Generally, retinal volumes exhibit a relatively low degree of variation. However, significant differences in the retinal volumes due to the monkey's origin were identified. Additionally, sex had a significant impact on the paracentral retinal volume. Therefore, the origin and sex of cynomolgus monkeys should be considered when evaluating the macaque retinal volumes based on this dataset.

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Conflict of interest statement

Research support was granted from Roche (Basel, Switzerland), especially with regard to data collection and the decision to publish. Roche had no role and did not interfere in the conceptualization or conduct of this study. Authors N.D., C.F., S.W., and MV are salaried employees of Roche, Switzerland. P.K. is a P.M.M. is a salaried employee of Supercomputing Systems, Zurich, Switzerland. The funder had no role in design, conduct or submission of the study. The other authors of this paper declare no competing interests. Outside of the present study, the authors declare the following competing interests: P.M.M. is a consultant at Zeiss Forum and holds intellectual property for machine learning at MIMO AG and VisionAI, Switzerland. H.P.N.S. is supported by the Swiss National Science Foundation (Project funding: “Developing novel outcomes for clinical trials in Stargardt disease using structure/function relationship and deep learning” #310030_201165 and National Center of Competence in Research Molecular Systems Engineering: “NCCR MSE: Molecular Systems Engineering (phase II)” #51NF40-182895), the Wellcome Trust (PINNACLE study), and the Foundation Fighting Blindness Clinical Research Institute (ProgStar study). H.P.N.S. is member of the scientific advisory boards of Astellas Pharma Global Development, Inc./Astellas Institute for Regenerative Medicine, Boehringer Ingelheim Pharma GmbH & Co; Gyroscope Therapeutics Ltd.; Janssen Research & Development, LLC (Johnson & Johnson); Novartis Pharma AG (CORE); Okuvision GmbH; and Third Rock Ventures, LLC. H.P.N.S. is a consultant for Gerson Lehrman Group; Guidepoint Global, LLC; and Tenpoint Therapeutics Limited. H.P.N.S. is member of the Data Monitoring and Safety Board/Committee of Belite Bio (CT2019-CTN-04690-1), ReNeuron Group Plc/Ora Inc. (NCT02464436), and F. Hoffmann-La Roche Ltd (VELODROME trial, NCT04657289; DIAGRID trial, NCT05126966) and member of the Steering Committee of Novo Nordisk (FOCUS trial; NCT03811561). All arrangements have been reviewed and approved by the University of Basel (Universitätsspital Basel, USB) and the Board of Directors of the Institute of Molecular and Clinical Ophthalmology Basel (IOB) in accordance with their conflict of interest policies. Compensation is being negotiated and administered as grants by USB, which receives them in its proper accounts. H.P.N.S. is co-director of the IOB, which is a non-profit foundation and receives funding from the University of Basel, the University Hospital Basel, Novartis, and the government of Basel-Stadt. P.V. received funding from the Swiss National Science Foundation (Grant 323530_199395) and the Janggen-Pöhn Foundation.

Figures

Figure 1
Figure 1
Visualization of the optical coherence tomography (OCT) retinal volume measurements. (a) A central retinal OCT volume was acquired (depicted with the green box). (b) In the first analysis step, the retina (highlighted in yellow) was separated from the rest of the tissue using a validated deep learning algorithm. In the second step, a classical algorithm automatically detects the deepest point within the fovea (marked as a red dot and labelled nulla). A field of interest with a diameter of 3000 µm was defined starting from nulla (illustrated with the purple rectangle). (c) And rotated within the OCT volume (symbolized as a green cylinder). (d) This enabled the volumetric measurements in ring areas (1, 2, and 3 mm) of four quadrants and slices, as illustrated.
Figure 2
Figure 2
Boxplots of zone volumes. Boxplots of sex- and origin-specific variations in retinal and choroidal zone volumes for the right (a) and left eyes (b).
Figure 3
Figure 3
Boxplots of quadrant volumes. Boxplots of sex- and origin-specific variations in retinal and choroidal quadrant volumes for the right (a) and left eyes (b).
Figure 4
Figure 4
Boxplots of slice volumes. Boxplots of sex- and origin-specific variations in retinal and choroidal slice volumes for the right (a) and left eyes (b).
Figure 5
Figure 5
Principal component analysis (PCA) scree plots of retinal volumes S1–S9. Plots show the cumulative eigenvalues of the nine principal components (PCs) for (a) right and (b) left eyes. Eigenvalues indicate the explained variability of the respective PC. Additionally, the first three PCs explain 91.8% and 91.9% of the variability in the right and left eyes, respectively.
See this image and copyright information in PMC

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