Direct modeling of foveal pit morphology from distortion-corrected OCT images

Abstract

Inherent distortions affect the spatial geometry of optical coherence tomography (OCT) images and consequently the foveal pit dimensions. Distortion correction provides an accurate anatomical representation of the retinal shape. A novel approach that automatically extracts foveal pit metrics from distortion-corrected OCT images using a sum of Gaussian function is presented. Foveal width, depth and slope were determined in 292 eyes with low fitting errors and high repeatability. Comparisons to undistorted scans revealed significant differences. To conclude, the internal OCT distortions affect the measurements of the foveal pit with their correction providing further insights into the role of foveal morphology in retinal pathologies and refractive development.

The Fovea in Retinopathy of Prematurity

“…Figure 9 plots the breadth and depth values obtained using a more involved procedure 33 (based upon fitting the ILM to the sum of three Gaussians) as a function of the depth and breadth values obtained directly from the parameters of a single Gaussian (in this case, FWHM for breadth). The correlation between the two methods was good for breadth ( r = 0.764) and especially for depth ( r = 0.992), but neither relationship fell directly on the diagonal.…”

“… Relations of foveal depth and breadth assessed using the single Gaussian model adopted herein ( abscissa ) and the sum of three Gaussians method of Breher et al 33 ( ordinate ) for term ( blue circles ), none ( green triangles ), mild ( yellow diamonds ), and severe ( red squares ) subjects. Solid lines have unity slope (on the primary axes) and zero intercept; stippled lines are orthogonal regressions through the data.…”

“…“FWHM ≈ 2.35 b ”) necessitating complex processes for converting fits to meaningful values, and decreased clarity in the observer's mind as to what, exactly, is being measured. To assess the impact of these tradeoffs, following the fits described above, we additionally fit the ILM using a sum of three Gaussians, and followed the method of Breher et al 33 to extract foveal depth and breadth. As shown in Figure 2 , their method depends upon identifying three peaks in the square of the second derivative of Equation 5 and, using the x values of these points to identify foveal landmarks on the original fit (a point near the nasal edge of the rim, the center of the trough, and a point near the temporal edge of the rim, respectively), and then relating these landmarks to one another to derive foveal parameters.…”

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

“…During the last decade, several models have been proposed to analyze foveal pit morphology [23,28,[30][31][32][33], which basically differ from each other in the selection of the macular region to be modelled and the underlying mathematical equations of the model. For instance, while the whole 2D thickness surface is modelled in [23], in [30,32,33] each B-scan is fitted separately. Other approaches, aiming for a high fitting accuracy, go even further by modelling independently either the two halves of a B-scan [31] or the region between the foveal center and the rim [28].…”

“…In regard to the mathematical equations, most models rely on Gaussian curves to account for the concave shape of the foveal pit. These include the difference of two Gaussians [30], the combination of a Gaussian and a polynomial term [23,32], the sum of three Gaussians [33] and a radial model based on the second derivative of a Gaussian [31]. In addition, alternative approaches using cubic Bézier curves [28] and P-splines have also been proposed [34].…”