The foveal center was identified anatomically on OCT and AOSLO images as described previously.
33 Individual cones (in confocal images) and RPE cells (in darkfield images) were identified via two different semiautomatic algorithms.
32,36,37 After semiautomatic cell detection identified the majority of cone and RPE cells, the software allowed the user to manually add, remove, or reposition cell centers, with the associated Delaunay or Voronoi tessellations updating in real time to aid mosaic visualization and cell identification (
Fig. 1A; see
Supplementary Fig. S1 for raw and annotated images of RPE cells for each participant). Manual cone selections were made and reviewed by two of the authors (HRP and RCB) when some cones in the foveal center were too dim or small to be adequately recognized by the automatic cell detection, based on the assumption that foveal cones are densely packed into a nearly hexagonal mosaic.
34,38 Non-confocal images were used to disambiguate cones from rods outside the foveal center.
39 After manual editing, inter- and intra-cell statistics were obtained from the Voronoi tessellation of the cell centers, notably the mean number of neighbors, mean inter-cell distance (ICD), and mean cell area.
30,40 Retinal cone density (cones/mm
2) was first estimated over a conventional 50 × 50-µm region of interest (ROI); only bounded cells (whose Voronoi boundaries were wholly contained within the given ROI) were included in the calculations (
Fig. 1C). For direct comparison with cone densities reported by others, foveal cone density was also computed for unbounded cones over a 40 × 40-µm ROI, a 10 × 10-µm ROI,
41 a circular ROI 50 µm in diameter,
34 and the smallest square ROI of variable area that encompassed 100 bound cones.
42 The RPE cell density (cells/mm
2) was estimated over 200 × 200 µm ROIs, and only bounded cells were included in the calculations. The RPE ROIs were chosen to maximally overlap with the cone ROIs from the foveal center and out to 5° eccentricity. ROIs did not span across different images. Within each ROI, the centers of individual cone and RPE cells were obtained through image processing (
Fig. 1B). All statistics related to cone and RPE cell counts are across 50 × 50-µm and 200 × 200-µm ROIs, respectively. In addition, the ICDs (µm) per cone and per RPE cell and the retinal eccentricity coordinates of each counted cell were extracted along the horizontal meridian (±0.50° in vertical direction). Assuming an asymmetric hexagonally packed mosaic,
43 the per-cell ICDs allowed us to calculate a local or fine-grained retinal cell density,
D (cones/mm
2) at the eccentricity of each counted cell, where
30 \begin{equation}D = \frac{{{{10}^6}}}{{{\rm{IC}}{{\rm{D}}^2}\cos \left( {\frac{\pi }{6}} \right)}}\end{equation}
Such cell-centric ICD data can provide a richer dataset than ROI-averaged density data that improves the robustness of the topographic cell profile modeling and estimated peak cell densities, even for those whose foveal cones were not clearly resolved.