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

Rajat Agarwala,Alexander Leube,Siegfried Wahl,Katharina Breher

doi:10.1364/boe.10.004815

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.

Highlights

The development of optical coherence tomography (OCT) has led to cross-sectional in-vivo imaging of the anterior and posterior segment
Altered foveal morphology plays a role in the diagnosis and classification of different pathologies, such as Parkinson disease [2], blue-cone monochromacy [3], albinism [4,5], retinopathy of prematurity [6] and other retinal diseases [7]
The average root mean square error (RMSE) between the fitted Sum of Gaussians (SoG) function and the detected inner limiting membrane (ILM) is as low as 2.16 ± 1.94 μm and 1.89 ± 1.35 μm for the right and left eye

Summary

Introduction

The development of optical coherence tomography (OCT) has led to cross-sectional in-vivo imaging of the anterior and posterior segment. The initial measurements of foveal parameters on OCT images were performed manually [11], followed by automated analyses [4,6]. Using a symmetrical Difference of Gaussians function, Dubis et al [3] firstly described the foveal contour by a mathematical model. Most studies analyzed the macular retinal thickness maps to indirectly describe the outer border of the inner limiting membrane as foveal contour [2,3,7,9,13,14], whilst others extracted manually the parameters like width, depth and slope from cross-sectional B-scans [11]. Only the automated and direct image-based analysis of HD OCT images along with the description of the foveal contour as a mathematical function allows the objective quantification of the fovea

Objectives

Methods

Results

Discussion

Conclusion