Related Experiment Video
Updated: Aug 25, 2025
Evaluation of Capillary and Other Vessel Contribution to Macular Perfusion Density Measured with Optical Coherence Tomography Angiography
Published on: February 18, 2022
A standardized method to quantitatively analyze optical coherence tomography angiography images of the macular and
Luiz Guilherme Marchesi Mello1, Taurino Dos Santos Rodrigues Neto2, Epitácio Dias da Silva Neto3
1Department of Specialized Medicine, Centro de Ciências da Saúde (CCS), Universidade Federal do Espírito Santo, Vitória, Brazil.
Background:
Optical coherence tomography angiography (OCTA) is a relatively new non-invasive imaging technique to evaluate retinal vascular complexes. However, there is still a lack of standardization and reproducibility of its quantitative evaluation. Furthermore, manual analysis of a large amount of OCTA images makes the process laborious, with greater data variability, and risk of bias. Therefore, the aim of this study is to describe a fast and reproducible quantitative analysis of the foveal avascular zone (FAZ), macular superficial and deep vascular complexes (mSVC and mDVC, respectively), and peripapillary superficial vascular complex (pSVC) in OCTA images.
Methods:
We survey models and methods used for studying retinal microvasculature, and software packages used to quantify microvascular networks. These programs have provided researchers with invaluable tools, but we estimate that they have collectively achieved low adoption rates, possibly due to complexity for unfamiliar researchers and nonstandard sets of quantification metrics. To address these existing limitations, we discuss opportunities to improve effectiveness, affordability, and reproducibility of microvascular network quantification with the development of an automated method to analyze the vessels and better serve the current and future needs of microvascular research. OCTA images of the macula (10°x10°, 15°x15°, or 20°x20° centered on the fovea) and peripapillary area (15 × 15º centered on optic nerve head) were exported from the device and processed using the open-source software Fiji. The mSVC, mDVC, and pSVC were automatically analyzed regarding vascular density in the total area and four sectors (superior, inferior, nasal, and temporal). We also analyzed the FAZ regarding its area, perimeter, and circularity in the SVC and DVC images.
Results:
We developed an automated model and discussed a step by step method to analyze vessel density and FAZ of the macular SVC and DVC, acquired with OCTA using different fields of view. We also developed an automated analysis of the peripapillary SVC.
Conclusion:
Our developed automated analysis of macular and peripapillary OCTA images will allow a fast, reproducible, and precise quantification of SVC, DVC, and FAZ. It would also allow more accurate comparisons between different studies and streamlines the processing of images from multiple patients with a single command.
