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Brian C Samuels, Nathan M Hammes, Lyne Racette, Gavriil Tsechpenakis; Analysis of Macular Retinal Thickness in Arcuate Bins for Glaucoma Detection. Invest. Ophthalmol. Vis. Sci. 2014;55(13):4755.
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© ARVO (1962-2015); The Authors (2016-present)
Current glaucoma analysis software on the Heidelberg Spectralis spectral-domain optical coherence tomographer (SD-OCT) identifies macular retinal thickness asymmetry. However, the current program analyzes retinal thickness as a grid centered on the fovea. We hypothesize that analysis of retinal thickness in bins that follow the anatomic arcuate RNFL distribution through the macula will result in a diagnostic SD-OCT imaging program that is more highly sensitive and specific for the diagnosis of glaucoma and analysis of glaucoma progression. The purpose of this study was to develop a prototype program capable of analyzing macular retinal thickness in arcuate bins.
Using the Heidelberg Spectralis SD-OCT, a 61-line (30 X 25 degree) raster scan centered over the fovea was used to obtain retinal thickness maps in 12 normal subjects. RAW Data was exported and MATLAB software (R2012a, version 7.14) used to autosegment the retinal images in TIFF format. Using deformable modeling techniques, the internal limiting membrane and the retinal pigmented epithelium were initially represented as deformable sheets that iteratively evolve to areas of interest based on contrast and intensity. Our retinal thickness map was segmented into a foveal-centered grid mirroring the Heidelberg Spectralis SD-OCT programs for preliminary validation. Retinal thickness maps were then segmented into 6 superior and inferior arcuate bins along the anatomic retinal nerve fiber layer (RNFL) distribution for asymmetry analysis.
Initial validation studies indicate a retinal thickness difference between Heidelberg’s boxed grid average (297±37μm) and our program (292±38 μm; p=0.011; See Figure 1A). Given 1 pixel represents 3.87μm retinal thickness, our retinal auto-segmentation is typically in agreement with the Heidelberg program within ~1.5 pixels. Our program successfully segments the retinal thickness map into superior and inferior arcuate segments that follow the RNFL anatomic distribution (see Figure 1B). Arcuate segments can be analyzed for asymmetry in the same eye or the contralateral fellow eye.
We have successfully developed an automated program capable of segmenting macular retinal thickness maps into arcuate segments based on RNFL distribution. Future testing will focus on determining the sensitivity and specificity of this program in detecting glaucoma and progression of the disease.
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