Foveal Pit Morphology Characterization: A Quantitative Analysis of the Key Methodological Steps

Abstract

Disentangling the cellular anatomy that gives rise to human visual perception is one of the main challenges of ophthalmology. Of particular interest is the foveal pit, a concave depression located at the center of the retina that captures light from the gaze center. In recent years, there has been a growing interest in studying the morphology of the foveal pit by extracting geometrical features from optical coherence tomography (OCT) images. Despite this, research has devoted little attention to comparing existing approaches for two key methodological steps: the location of the foveal center and the mathematical modelling of the foveal pit. Building upon a dataset of 185 healthy subjects imaged twice, in the present paper the image alignment accuracy of four different foveal center location methods is studied in the first place. Secondly, state-of-the-art foveal pit mathematical models are compared in terms of fitting error, repeatability, and bias. The results indicate the importance of using a robust foveal center location method to align images. Moreover, we show that foveal pit models can improve the agreement between different acquisition protocols. Nevertheless, they can also introduce important biases in the parameter estimates that should be considered.

Central Macular Topographic and Volumetric Measures: New Biomarkers for Detection of Glaucoma

“…A recent study on foveal morphology showed the importance of accounting for artifacts seen on RGC probability maps in healthy subjects; 44 the proposed modeling of the perifoveal region by Yadav et al is an important step toward individualized structure-function maps of the macula as further development of the current deep learning algorithms may help prediction of functional damage in the macular region. 14 , 17 …”

“… 22 A similar result was shown in a recent paper by Romero-Boscenes et al, who performed a comparison between current state-of-the-art methods for foveal shape modeling in terms of accuracy and robustness. 17 …”

Spatial characterization of the effect of age and sex on macular layer thicknesses and foveal pit morphology

“…The images were loaded into MATLAB 2020b (The Mathworks Inc., Natick, MA, United States) and subsequently analyzed using the open-source RETIMAT Toolbox ( https://github.com/drombas/retimat ). All scans were aligned by automatically locating the foveal center as the minimum value of a smoothed total retinal thickness (TRT) map [ 33 ]. Left eyes were flipped to match right eyes.…”