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. 2016 Feb;57(2):675-82.
doi: 10.1167/iovs.15-17671.

Does the Location of Bruch's Membrane Opening Change Over Time? Longitudinal Analysis Using San Diego Automated Layer Segmentation Algorithm (SALSA)

Akram Belghith, Christopher Bowd, Felipe A Medeiros, Naama Hammel, Zhiyong Yang, Robert N Weinreb, Linda M Zangwill

Does the Location of Bruch's Membrane Opening Change Over Time? Longitudinal Analysis Using San Diego Automated Layer Segmentation Algorithm (SALSA)

Akram Belghith et al. Invest Ophthalmol Vis Sci. 2016 Feb.

Abstract

Purpose: We determined if the Bruch's membrane opening (BMO) location changes over time in healthy eyes and eyes with progressing glaucoma, and validated an automated segmentation algorithm for identifying the BMO in Cirrus high-definition coherence tomography (HD-OCT) images.

Methods: We followed 95 eyes (35 progressing glaucoma and 60 healthy) for an average of 3.7 ± 1.1 years. A stable group of 50 eyes had repeated tests over a short period. In each B-scan of the stable group, the BMO points were delineated manually and automatically to assess the reproducibility of both segmentation methods. Moreover, the BMO location variation over time was assessed longitudinally on the aligned images in 3D space point by point in x, y, and z directions.

Results: Mean visual field mean deviation at baseline of the progressing glaucoma group was -7.7 dB. Mixed-effects models revealed small nonsignificant changes in BMO location over time for all directions in healthy eyes (the smallest P value was 0.39) and in the progressing glaucoma eyes (the smallest P value was 0.30). In the stable group, the overall intervisit-intraclass correlation coefficient (ICC) and coefficient of variation (CV) were 98.4% and 2.1%, respectively, for the manual segmentation and 98.1% and 1.9%, respectively, for the automated algorithm.

Conclusions: Bruch's membrane opening location was stable in normal and progressing glaucoma eyes with follow-up between 3 and 4 years indicating that it can be used as reference point in monitoring glaucoma progression. The BMO location estimation with Cirrus HD-OCT using manual and automated segmentation showed excellent reproducibility.

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Figures

Figure 1
Figure 1
Distribution of the absolute value of the differences in micrometers between SALSA and manual BMO segmentation location for x-, y-, and z-axes (top to bottom, ΔBMOx, ΔBMOy, and ΔBMOz, respectively) using the 50 eyes of stable group.
Figure 2
Figure 2
Examples of automated BMO segmentation. The red points are the manual segmentation results and the blue points are the automated segmentation results using SALSA algorithm. For the top image, the BMO failure of the SALSA is due to a disconnection of the BM layer. In the bottom image, manual and SALSA segmentation methods showed similar results.
Figure 3
Figure 3
Distributions of the BMO location change over time for healthy (top) and progressing glaucomatous eyes (bottom) along the x-, y-, and z-axes.
See this image and copyright information in PMC

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