Fig. 1.

Complete morphometric analysis of an entire human RPE monolayer. (A) Representative image of an entire human RPE flatmount (red: phalloidin). Approximately 2 to 3.5 million cells were captured in about 200,000 images, projected in two-dimensional space, and tiled together. (B) Representative higher magnification image of phalloidin (red)-stained RPE cells. (C) REShAPE, a U-net convolutional neural network (CNN), recognizes and segments RPE cell borders from fluorescent images and analyzes RPE cell morphometry for the entire human RPE flatmount. (Conv 3x3, 3x3 convolutions; ReLU, rectified line unit.) (D and E) REShAPE-generated image for the entire human RPE flatmount with cell borders segmented for each RPE cell (D) and a representative higher magnification image (E). (F) Schematic representation of four distinct cell morphometry parameters (cell area, AR, hexagonality score, and number of neighbors) used for RPE cell shape analysis, specific examples highlighted. (G, I, K, and M) REShAPE-segmented cadaver human RPE flatmount images that are color-coded for cell area, AR, hexagonality score, and number of neighbors illustrate shape metrics of individual RPE cells in human eyes. Heatmaps on the top left corner of each image show range of values used for these four morphometry parameters. Arrowhead, fovea; *, optic nerve; arrow, peripheral ring of small RPE cells. (H, J, L, and N) Zoomed-in color-coded images display RPE shape metrics at single-cell level.