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. 2020 Aug 11;7:262. doi: 10.1038/s41597-020-00608-w

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© The Author(s) 2020

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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Fig. 1 — Workflow for ground truth image annotation. (a) Raw image visualizing HaCaT cytospinned nuclei. (b) A machine learning framework was used to annotate the raw image, learning from user interaction within three consecutive steps: S1. foreground extraction, S2. connected component classification (red = non-usable objects, blue = nuclei aggregations, green = single nuclei) and S3. splitting of aggregated objects into single nuclei, resulting in an annotation mask. (c) Zoom-in of the SVG-file showing the nuclear image overlaid with polygons representing each annotated nucleus. Polygons were modified by expert biologists to fit effective nuclear borders. Challenging decisions on how to annotate nuclei, mainly occurring due to aggregated or overlapped nuclei, were presented to an expert pathologist and corrected to obtain the final ground truth. (d) The curated SVG-file was transformed into a labeled nuclear mask.