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. 2019 Jun 24;9:9096. doi: 10.1038/s41598-019-43958-1

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© The Author(s) 2019

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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Schematic of processing steps used to generate en face OCT(A) images. The OCT volume is segmented, typically automatically, to form a set of contours. Usually the OCT signal, and not the OCTA signal, is used for segmentation (as shown here). The OCT and OCTA volumes are then projected (using averaging or histogram methods) between the segmented contours. This transforms volumetric (3-D) OCT(A) data into en face (2-D) OCT(A) data. Because the en face OCTA depends on the OCTA generation and segmentation steps, which in turn depend on the underlying OCT data, artifacts in the OCT data ultimately cause artifacts in the en face OCT(A) data.