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Improved Automated Foveal Avascular Zone Measurement in Cirrus Optical Coherence Tomography Angiography Using the Level Sets Macro.

Authors :
Lin A
Fang D
Li C
Cheung CY
Chen H
Source :
Translational vision science & technology [Transl Vis Sci Technol] 2020 Nov 13; Vol. 9 (12), pp. 20. Date of Electronic Publication: 2020 Nov 13 (Print Publication: 2020).
Publication Year :
2020

Abstract

Purpose: To evaluate automated measurements of the foveal avascular zone (FAZ) using the Level Sets macro (LSM) in ImageJ as compared with the Cirrus optical coherence tomography angiography (OCTA) inbuilt algorithm and the Kanno-Saitama macro (KSM).<br />Methods: The eyes of healthy volunteers were scanned four times consecutively on the Zeiss Cirrus HD-OCT 5000 system. The FAZ metrics (area, perimeter, and circularity) were measured manually and automatically by the Cirrus inbuilt algorithm, the KSM, and the LSM. The accuracy and repeatability of all methods and agreement between automated and manual methods were evaluated.<br />Results: The LSM segmented the FAZ with an average Dice coefficient of 0.9243. Compared with the KSM and the Cirrus inbuilt algorithm, the LSM outperformed them by 0.02 and 0.19, respectively, for Dice coefficients. Both the LSM (intraclass correlation coefficient [ICC] = 0.908; coefficient of variation [CoV] = 9.664%) and manual methods (ICC ≥ 0.921, CoV ≤ 8.727%) showed excellent repeatability for the FAZ area, whereas the other methods presented moderate to good repeatability (ICC ≤ 0.789, CoV ≥ 15.788%). Agreement with manual FAZ area measurement was excellent for both the LSM and KSM but not for the Cirrus inbuilt algorithm (LSM, ICC = 0.930; KSM, ICC = 0.928; Cirrus, ICC = 0.254).<br />Conclusions: The LSM exhibited greater accuracy and reliability compared to the KSM and inbuilt automated methods and may be an improved and accessible option for automated FAZ segmentation.<br />Translational Relevance: The LSM may be a suitable automated and customizable tool for FAZ quantification of Cirrus HD-OCT 5000 images, providing results comparable to those for manual measurement.<br />Competing Interests: Disclosure: A. Lin (P); D. Fang, None; C. Li, None; C.Y. Cheung, None; H. Chen (P)<br /> (Copyright 2020 The Authors.)

Details

Language :
English
ISSN :
2164-2591
Volume :
9
Issue :
12
Database :
MEDLINE
Journal :
Translational vision science & technology
Publication Type :
Academic Journal
Accession number :
33240573
Full Text :
https://doi.org/10.1167/tvst.9.12.20