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. 2019 Dec 4;9:18295. doi: 10.1038/s41598-019-54244-5

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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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Comparison of segmentations obtained from DeepSynth with those obtained using software commonly used in biomedical imaging. Images collected from different depths of the volume shown in Fig. 1 are shown in the top row. Segmentation results obtained using DeepSynth, FARSIGHT, Squassh and CellProfiler are shown in the rows below. Individual objects are rendered in different colors to facilitate evaluation of discrimination of individual nuclei. Supplementary Videos 2, 3 and 4 show animations of volume renderings of segmentations obtained using DeepSynth with those obtained using FARSIGHT, Squassh and CellProfiler, respectively, for a subvolume ranging from 130 to 162 microns depth in the sample.