The aim of this study was to describe an automated method for extracting quantitative measures of foveal morphology from optical coherence tomography (OCT) images of the human retina.
We performed a methodological study and retrospective investigation of selected cases. Sixty-five human subjects were included: 61 healthy subjects and four female carriers of blue-cone monochromacy (BCM). Thickness data from B-scans traversing the foveal pit were fitted to a mathematical model designed to capture the contour of the foveal surface. From this model, various metrics of foveal morphology were extracted (pit depth, diameter and slope).
Mathematical descriptions of foveal morphology enabled quantitative and objective evaluation of foveal dimensions from archived OCT data sets. We found a large variation in all aspects of the foveal pit (depth, diameter and slope). In myopes and BCM carriers, foveal pits were slightly less deep and had a more shallow slope, although these differences were not significant.
Offline analysis of OCT data sets enables quantitative assessment of foveal morphology. The algorithm works on the Stratus™ and Cirrus™ macular thickness protocols, as well as the Spectralis® and Bioptigen© radial-line scan protocols, and can be objectively applied to existing data sets. These metrics will be useful in following changes associated with diseases such as retinopathy of prematurity and high myopia, as well as in studying normal postnatal development of the human fovea.