Fig 1.
Choroidal volume measurements in a right eye.
a. A machine learning algorithm was trained to detect the choroid from an obtained macula volume OCT scan (highlighted in yellow; brown = vitreous, blue = retina). For a better overview, only a single B-scan is illustrated here. Consequently, a classic algorithm automatically defined the deepest location within the foveolar depression which was marked a nulla (arrow, red spot). b. Starting from nulla, a rectangle (depicted in pink) was defined to the side with a total length of 3000 μm. c. This rectangle was rotated axially centered on nulla to segment the choroid within the OCT volume allowing measurements only the central and paracentral subfields. d. From the segmented choroid volume, choroidal sub-fields were analyzed, marked as circular zones, quadrants, and slices. (outer zones 10–13 were not investigated).
Fig 2.
Boxplots of sex-specific variations in choroid volumes.
For right (2a, OD) and left eyes (2b, OS) measured as circular zones centered on the foveolar depression.
Fig 3.
Boxplots of sex-specific variations in choroid volumes.
For right (3a, OD) and left eyes (3b, OS) measured as quadrants.
Fig 4.
Boxplots of origin-specific variations in choroid volumes.
For right (4a, OD) and left eyes (4b, OS) measured as slices.
Table 1.
Summary presentation of the choroid volume zone values male compared to female monkeys and origin.
Table 2.
Summary presentation of the choroid volume quadrant values male compared to female monkeys and origin.
Table 3.
Summary presentation of the choroid volume slice values male compared to female monkeys and origin.
Table 4.
Pearson correlation among the sixteen coefficients (3 zone, 4 quadrants, 9 slice coefficients).
Fig 5.
Principal Component Analysis (PCA) plots of choroidal volumes S1 –S9.
(a) and (c) are scree plots showing the cumulative eigenvalues of the nine principal components (PCs) for right and left eyes, respectively. Eigenvalues indicate the explained variability of the respective PC. The first two PCs explain 88.1% and 88.8% of the variability in right and left eyes, respectively. (b) and (d) show projections of the data onto the first two principal components for right and left eyes, respectively.
Table 5.
Principal component analysis coefficients of the first two principal components for right and left eyes.
Table 6.
MANOVA results.
Table 7.
Summary of p-values in two-way analysis of variance (ANOVA) for measured choroidal thickness parameters in right (first two rows) and left (last two rows) eyes in relation to sex and origin.