Automated geographic atrophy segmentation for SD-OCT images using region-based C-V model via local similarity factor

Abstract

Age-related macular degeneration (AMD) is the leading cause of blindness among elderly individuals. Geographic atrophy (GA) is a phenotypic manifestation of the advanced stages of non-exudative AMD. Determination of GA extent in SD-OCT scans allows the quantification of GA-related features, such as radius or area, which could be of important value to monitor AMD progression and possibly identify regions of future GA involvement. The purpose of this work is to develop an automated algorithm to segment GA regions in SD-OCT images. An en face GA fundus image is generated by averaging the axial intensity within an automatically detected sub-volume of the three dimensional SD-OCT data, where an initial coarse GA region is estimated by an iterative threshold segmentation method and an intensity profile set, and subsequently refined by a region-based Chan-Vese model with a local similarity factor. Two image data sets, consisting on 55 SD-OCT scans from twelve eyes in eight patients with GA and 56 SD-OCT scans from 56 eyes in 56 patients with GA, respectively, were utilized to quantitatively evaluate the automated segmentation algorithm. We compared results obtained by the proposed algorithm, manual segmentation by graders, a previously proposed method, and experimental commercial software. When compared to a manually determined gold standard, our algorithm presented a mean overlap ratio (OR) of 81.86% and 70% for the first and second data sets, respectively, while the previously proposed method OR was 72.60% and 65.88% for the first and second data sets, respectively, and the experimental commercial software OR was 62.40% for the second data set.

Green-Light Autofluorescence Versus Combined Blue-Light Autofluorescence and Near-Infrared Reflectance Imaging in Geographic Atrophy Secondary to Age-Related Macular Degeneration

“…These are not visible in the BAF image (D2). the setting of GA. 26 Typically, OCT-based segmentation methods rely on en face fundus or sub-RPE projection images that depict so-called hypertransmission into the choroid in regions of GA. 13,26,29 Hereby, large choroidal vessels are typically hyposcattering and may result in segmentation artifacts. 13 Thus, future studies should evaluate automated OCT-based segmentation in comparison with BAF or GAF images, as the latter usually depict a higher contrast than OCT images.…”

Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search

“…Mathematic model based methods construct a fixed or adaptive model based on prior assumptions for the structure of the input images, and include A-scan [16,17], active contour [18][19][20][21], sparse high order potentials [22], and 2D/3D graph [23][24][25][26][27][28][29][30] based methods. Machine learning based methods formulate layer segmentation as a classification problem, where features are extracted from each layer or its boundaries and used to train a classifier (e.g.…”

Multiple surface segmentation using convolution neural nets: application to retinal layer segmentation in OCT images

“…Graph-cut [ 6 ] is also used for surface segmentation where regions are segmented using an optimizing framework and surfaces are recovered as a post processing step by finding the boundary of segmented regions. (ii) Contour modeling approaches [ 7 , 8 ] - use an adaptive model based on prior shape information of the target structure in input images. In addition to prior target structure information, iterative models have also been used to consider weighted functions of image intensity and gradient properties [ 9 ].…”