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. 2019 Jul 15;9:10237. doi: 10.1038/s41598-019-46689-5

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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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Application, implementation and performance of cell segmentation for different cell types from different organism imaged at low and high resolution. (A) Overlay of widefield (grey), DAPI (blue), nuclear (yellow) and cytoplasmic (magenta) segmented S. cerevisiae, S. pombe, mESC and Jurkat cells imaged at 100x and mESC and Jurkat cells imaged at 20x. (B) Image processing can be performed on a desktop computer or on a computing cluster for each individual image stack resulting in significant throughput. Segmenting one image takes on overage 30 minutes. For an average time course experiment consisting of 64 images of yeast cells, parallelization speeds up the image processing 64 fold.