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. 2016 Apr 1;57(4):2283-9.
doi: 10.1167/iovs.15-19008.

Semiautomatic Segmentation of Rim Area Focal Hyperautofluorescence Predicts Progression of Geographic Atrophy Due to Dry Age-Related Macular Degeneration

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Semiautomatic Segmentation of Rim Area Focal Hyperautofluorescence Predicts Progression of Geographic Atrophy Due to Dry Age-Related Macular Degeneration

Michael J Allingham et al. Invest Ophthalmol Vis Sci. .

Abstract

Purpose: To develop image analysis software usable by nonexpert graders to segment geographic atrophy (GA) from dry AMD and to quantify rim area focal hyperautofluorescence (RAFH) surrounding GA on fundus autofluorescence (FAF) images. To compare the GA progression predictions based on RAFH with those of a validated qualitative classification system.

Methods: Retrospective analysis of serial FAF images from 49 eyes of 30 subjects with GA was performed using MATLAB-based software (MathWorks, Natick, MA, USA). Correlation between RAFH and progression of GA was analyzed using Spearman correlation. Comparisons of lesion growth rate between RAFH tertiles used generalized estimating equations and Kruskal-Wallis testing. Interobserver variability in lesion size, growth rate and RAFH were compared between two expert and one nonexpert grader using Bland-Altman statistics.

Results: Rim area focal hyperautofluorescence was positively correlated with GA progression rate (ρ = 0.49, P < 0.001). Subjects in the middle or highest RAFH tertile were at greater risk of progression (P = 0.005 and P = 0.001, respectively). Mean difference in RAFH was 0.012 between expert and -0.005 to 0.017 between expert and nonexperts. Mean difference in lesion size (mm2) was 0.11 between expert and -0.29 to 0.41 between expert and nonexperts. Mean difference in lesion growth rate (mm2/mo) was 0.0098 between expert and -0.027 to 0.037 between expert and nonexperts. Risk stratification based on RAFH tertile was 96% identical across all graders.

Conclusions: Our semiautomated image analysis software facilitates stratification of progression risk based on RAFH and enabled a nonexpert grader with minimal training to obtain results comparable to expert graders. Predictions based on RAFH were similar to those of a validated qualitative classification system.

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Figures

Figure 1
Figure 1
Representative baseline images and output from the semiautomated software analysis algorithm from subjects of each RAFH tertile. Baseline photos are shown on the left. Geographic atrophy is outlined in red and the outer border of the rim area is outlined in green. Rim area hyperautofluorescence is outlined in blue. Subject one and two had high and medium RAFH, respectively, and significant progression in lesion size. Subject three had low RAFH and minimal lesion growth.
Figure 2
Figure 2
Graph of GA progression versus RAFH. Spearman correlation coefficient was 0.49 (P < 0.001).

Comment in

  • Understanding RPE Lipofuscin.
    Smith RT, Farsiu S, Allingham M. Smith RT, et al. Invest Ophthalmol Vis Sci. 2016 Dec 1;57(15):6766. doi: 10.1167/iovs.16-21081. Invest Ophthalmol Vis Sci. 2016. PMID: 27978557 No abstract available.

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