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Helena Giannakaki-Zimmermann, Wolfgang Huf, Karen B. Schaal, Kaspar Schürch, Chantal Dysli, Muriel Dysli, Anita Zenger, Lala Ceklic, Carlos Ciller, Stephanos Apostolopoulos, Sandro De Zanet, Raphael Sznitman, Andreas Ebneter, Martin S. Zinkernagel, Sebastian Wolf, Marion R. Munk, on behalf of the Bern Photographic Reading Center; Comparison of Choroidal Thickness Measurements Using Spectral Domain Optical Coherence Tomography in Six Different Settings and With Customized Automated Segmentation Software. Trans. Vis. Sci. Tech. 2019;8(3):5. doi: https://doi.org/10.1167/tvst.8.3.5.
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We investigate which spectral domain-optical coherence tomography (SD-OCT) setting is superior when measuring subfoveal choroidal thickness (CT) and compared results to an automated segmentation software.
Thirty patients underwent enhanced depth imaging (EDI)-OCT. B-scans were extracted in six different settings (W+N = white background/normal contrast 9; W+H = white background/maximum contrast 16; B+N = black background/normal contrast 12; B+H = black background/maximum contrast 16; C+N = Color-encoded image on black background at predefined contrast of 9, and C+H = Color-encoded image on black background at high/maximal contrast of 16), resulting in 180 images. Subfoveal CT was manually measured by nine graders and by automated segmentation software. Intraclass correlation (ICC) was assessed.
ICC was higher in normal than in high contrast images, and better for achromatic black than for white background images. Achromatic images were better than color images. Highest ICC was achieved in B+N (ICC = 0.64), followed by B+H (ICC = 0.54), W+N, and W+H (ICC = 0.5 each). Weakest ICC was obtained with Spectral-color (ICC = 0.47). Mean manual CT versus mean computer estimated CT showed a correlation of r = 0.6 (P = 0.001).
Black background with white image at normal contrast (B+N) seems the best setting to manually assess subfoveal CT. Automated assessment of CT seems to be a reliable tool for CT assessment.
To define optimized OCT analysis settings to improve the evaluation of in vivo imaging.
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