Digital twins of human corneal endothelium from generative adversarial networks

Abstract

The human corneal endothelium, the posterior most layer of the cornea, is a monolayer of flat cells that are essential for maintening its transparency over time. Endothelial cells are easily visualized in patients using a specular microscope, a routine device, but accurate cell counting and cell morphometry determination has remained challenging since decades. The first automatic segmentations used mathematical morphology techniques, or the principles of the Fourier transform. In recent years, convolutional neural networks have further improved the results, but they need a large learning database, which takes a long time to collect. Thus, this work proposes a method for simulating digital twins of the images observed in specular microscopy, in order to enrich medical databases.