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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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Workflow for automatic detection of low-OCT-signal artifacts. (A) Input en face OCT image. (B) Spatial variance mask to detect segmentation errors, formed by computing the spatial variance of the OCT image (9 pixel × 9 pixel kernel), and then binarizing the resulting variance image using an empirical threshold determined from the qualitative observations of a single grader. Additional morphological steps (closing and erosion) are used to remove spurious regions. (C) Low OCT signal mask, formed by an adaptive binarization of the input OCT image. In particular, a dynamic threshold (3 pixel × 3 pixel kernel) is computed relative to the mean intensity of the input en face OCT image. (D) Output artifact mask, formed by combing the masks of panels (B,C); again, morphological processing (erosion) is used to remove spurious regions. (E) Overlay of the output artifact mask on the input en face OCT image.