Automated intraretinal segmentation of SD-OCT images in normal and age-related macular degeneration eyes

Abstract

This work introduces and evaluates an automated intra-retinal segmentation method for spectral-domain optical coherence (SD-OCT) retinal images. While quantitative assessment of retinal features in SD-OCT data is important, manual segmentation is extremely time-consuming and subjective. We address challenges that have hindered prior automated methods, including poor performance with diseased retinas relative to healthy retinas, and data smoothing that obscures image features such as small retinal drusen. Our novel segmentation approach is based on the iterative adaptation of a weighted median process, wherein a three-dimensional weighting function is defined according to image intensity and gradient properties, and a set of smoothness constraints and pre-defined rules are considered. We compared the segmentation results for 9 segmented outlines associated with intra-retinal boundaries to those drawn by hand by two retinal specialists and to those produced by an independent state-of-the-art automated software tool in a set of 42 clinical images (from 14 patients). These images were obtained with a Zeiss Cirrus SD-OCT system, including healthy, early or intermediate AMD, and advanced AMD eyes. As a qualitative evaluation of accuracy, a highly experienced third independent reader blindly rated the quality of the outlines produced by each method. The accuracy and image detail of our method was superior in healthy and early or intermediate AMD eyes (98.15% and 97.78% of results not needing substantial editing) to the automated method we compared against. While the performance was not as good in advanced AMD (68.89%), it was still better than the manual outlines or the comparison method (which failed in such cases). We also tested our method's performance on images acquired with a different SD-OCT manufacturer, collected from a large publicly available data set (114 healthy and 255 AMD eyes), and compared the data quantitatively to reference standard markings of the internal limiting membrane and inner boundary of retinal pigment epithelium, producing a mean unsigned positioning error of 6.04 ± 7.83µm (mean under 2 pixels). Our automated method should be applicable to data from different OCT manufacturers and offers detailed layer segmentations in healthy and AMD eyes.

Semi‐automated quantification of geographic atrophy with blue‐light autofluorescence and spectral‐domain optical coherence tomography: a comparison between the region finder and the advanced retinal pigment epithelium tool in the clinical setting

“…; de Sisternes et al. ). Computer‐assisted GA analysis is a promising approach, but the number of commercially available software is very limited and there is a need for quality and performance assessments in ‘real‐world’ settings, as pointed out in a recently published review (Wintergerst et al.…”

Impact of Drusen Volume on Quantitative Fundus Autofluorescence in Early and Intermediate Age-Related Macular Degeneration

“…D rusen and pigmentary alterations are well-known hallmark signs of age-related macular degeneration (AMD) and are used in clinical classifications to calculate individual risk factors. 1,2 Drusen have been studied extensively in relation to disease progression, as for example by algorithms automatically quantifying drusen on fundus color photos, 3,4 spectral-domain optical coherence tomography (SD-OCT), [5][6][7][8][9][10] and polarizationsensitive OCT (PS-OCT) images. 11 Changes in drusen volume have been examined as a possible marker for disease progression to create new end points for clinical studies.…”

Pixel-wise segmentation of severely pathologic retinal pigment epithelium and choroidal stroma using multi-contrast Jones matrix optical coherence tomography

“…However, in heavily damaged or degenerated tissue, the accuracy of the segmentation becomes inadequate [4,8]. Some methods have overcome this limitation, but they generally require an iterative process and are time-consuming [9][10][11][12].…”