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. 2018 Jan 12;8:605. doi: 10.1038/s41598-017-19103-1

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© The Author(s) 2018

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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Test for accuracy of the HyphaTracker toolbox on fungal growth simulation. Hyphae were described as two-dimensional worm-like-chains with fixed persistence length and a variable contour length to resemble experimental observations as close as possible. Simulated image series exhibiting different SNRs as indicated were analysed by the HyphaTracker. The time resolved area data were fit using a lag-exponential growth model⁵² and the parameters obtained were compared with ground truth (GT). (a) Lag-time. (b) Rate constant. (c) Initial-area (offset). Note, that the accuracy of HyphaTracker decreased with lower SNRs as indicated by increasing standard deviations (a,b) and, in comparison to ground truth data, underestimated initial areas (c).