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. 2020 Aug 3;10:13062. doi: 10.1038/s41598-020-70084-0

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© The Author(s) 2020

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Video processing and software analysis. (A) ‘Is Fundus’ function determines whether the image is that of a fundus (true) or not (false). This step excludes all non-fundus images. (B) The optic disc is then localized in order to measure the vessels from the same distance from the center on all images (2 radiuses). (C) Quality parameters filter images based on image quality (scores the image between 0–100). (D) Rotation: The image with the highest quality was chosen as reference, Max(Q). All images are perceived as a coordinate system (x, y, α), where α was the rotation All images were rotated to match the reference image to achieve more accurate measurements of the same points. (E) Twenty-five points are chosen for each vessel and the diameter in pixels is measured. (F) An image of how the CNTK deep learning algorithm recognizes vessels of sufficient quality on 64 × 64 px images for analysis.