Aims: To describe an automated method for extracting quantitative measures of foveal morphology from optical coherence tomography (OCT) images of the human retina.
Methods: 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. Thickness data from B scans traversing the foveal pit was fit 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).
Results: Mathematical descriptions of foveal morphology enabled quantitative and objective evaluation of foveal dimensions from archived OCT data sets. We found large variation in all aspects of the foveal pit (depth, diameter, and slope). In myopes and BCM carriers, foveal pits were slightly wider and more shallow, but the difference was only significant in the myopes.
Conclusions: 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 like retinopathy of prematurity and high myopia, as well as studying normal postnatal development of the human fovea.