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. 2023 Mar 20;20:20. doi: 10.1186/s12987-023-00421-8

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© The Author(s) 2023, corrected publication 2023

Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

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Fig. 8 — Evaluation of the image analysis error when estimating endfoot areas from 2D images. An example of the region of interest for one particular realization is shown in the top image. $P = 100$ means all polygons in the image have been counted. $P = 30$ means only the polygons centered in the stripe of thickness $0.3 d_{v}$ in the middle of the vessel between the vessel outlines have been considered in the analysis. The top image shows $P = 50$ applied to the sample of Fig. 1 and the counted polygons are marked with a dot in their centroid. Counting all polygons leads to the most severe underestimation. The practice of discarding polygons close to the border reduces the error of the image analysis (underestimation fraction closer to 1). Solid lines are least squares curve fits with cubic polynomials