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Purpose: Spectral domain optical coherence tomography can be used to measure both choroidal thickness and drusen load. The authors conducted an exploratory study using spectral domain optical coherence tomography to determine if a correlation between choroidal thickness and drusen load exists in patients with dry age-related macular degeneration., Methods: Forty-four patients with dry age-related macular degeneration were recruited. The drusen area and volume were determined using the automated software algorithm of the spectral domain optical coherence tomography device, and choroidal thickness was measured using enhanced depth imaging. Correlations were determined using multivariable and univariable analyses., Results: The authors found an inverse correlation between choroidal thickness and drusen load (r = -0.35, P = 0.04). Drusen load was also correlated with visual acuity (r = 0.32, P = 0.04). A correlation between choroidal thickness and visual acuity was suggested (r = -0.22, P = 0.21)., Conclusion: Spectral domain optical coherence tomography can be used to assess the correlation between drusen load and choroidal thickness, both of which show a relationship with visual acuity. The measurement of these outcomes may serve as important outcome parameters in routine clinical care and in clinical trials for patients with dry age-related macular degeneration.

Purpose: We compared the reproducibility and mutual agreement of the subfoveal choroidal thickness measurements by expert raters and an automated algorithm in enhanced depth imaging optical coherence tomography (EDI-OCT) images of eyes with nonneovascular age-related macular degeneration (AMD)., Methods: We recruited 44 patients with nonneovascular AMD and EDI-OCT images were acquired. Subfoveal choroidal thickness was measured manually by two expert raters and automatically by a graph-cut-based algorithm. Drusen area was measured using the automated software (version 6) of Cirrus SD-OCT. The manual and automated choroidal thickness measurements were compared in reproducibility, mutual agreement, and correlation with drusen area., Results: The mean subfoveal choroidal thickness was 246 ± 63 μm for the first rater, 214 ± 68 for the second rater, and 209 ± 53 for the automated algorithm. Intraclass correlation coefficients (ICC) and 95% confidence intervals (CI) were 0.96 (CI 0.94-0.98) between the raters, 0.85 (CI 0.77-0.90) between the first rater and the automated algorithm, and 0.84 (CI 0.75-0.89) between the second rater and the automated algorithm. Repeat scan measurement ICCs were 0.91 (CI 0.86-0.94) for the first rater, 0.96 (CI 0.94-0.97) for the second rater, and 0.87 (CI 0.80-0.92) for the automated algorithm. Manual and automated measurements were correlated with drusen area., Conclusions: The automated algorithm generally yielded smaller choroidal thickness than the raters with a moderate level of agreement. However, its repeat scan measurement repeatability was comparable to that of the manual measurements. The mean difference between the raters indicated possible biases in different raters and rating sessions. The correlation of the automated measurements with the drusen area was comparable to that of the manual measurements. Automated subfoveal choroidal thickness measurement has potential use in clinical practice and clinical trials, with possibility for reduced time and labor cost.

Age-related macular degeneration (AMD) is the leading cause of blindness in developed countries. Subretinal fluid (SRF) and sub-retinal pigment epithelium (sub-RPE) fluid are signs of AMD and can be detected in optical coherence tomography images. However, manual detection and segmentation of SRFs and sub-RPE fluids are laborious and time consuming. In this paper, a novel pipeline is proposed for automatic detection of SRFs and sub-RPE fluids. First, top and bottom layers of retina are segmented using a graph cut method. Then, a Split Bregman-based segmentation method is used to segment dark regions between layers. These segmented regions are considered as potential fluid candidates, on which a set of features are generated. After that, a random forest classifier is trained to distinguish between the true fluid regions from the falsely detected fluid regions. This method shows reasonable performance in a leave-one-out evaluation using a dataset from 21 patients.