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. 2018 Mar 28;8:5319. doi: 10.1038/s41598-017-18815-8

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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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The OrganoSeg pipeline segments brightfield 3D-culture images more accurately than existing methods. (a) Key steps in the image-segmentation pipeline applied to a representative brightfield image. Users can opt to split aggregates (blue) or remove debris (red) in the software. (b,c) Comparison of OrganoSeg with competing alternatives^13–15 according to spheroid call rates in (b) and Kolmogorov-Smirnov statistics (K-S stat) of segmented area distributions in (c). The number of images segmentable by each algorithm is shown to the right of c. Images were segmented with following OrganoSeg parameters: Otsu threshold = 1, Max-window size = 250 pixels, Size-exclusion threshold = 10 pixels. Data in (b) are shown as the median call rate with 95% confidence intervals in brackets from n = 19 images of MCF10A-5E spheroids. Data in (c) are shown as median boxplots of K-S stat from 1000 bootstrap replicates of n = 861 total spheroids, with significant differences from manual segmentation (gray) assessed by K-S test.