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. 2020 Sep 1;10:14408. doi: 10.1038/s41598-020-71347-6

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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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U-Net based image analysis workflow. (a) Raw images of colon or small intestine with DAPI stained nuclei and anti-EpCAM stained epithelial were annotated separately by biologists using ImageJ. Annotated images were used to fine-tune or to validate U-Net models residing in AWS EC2. (b) The performance of epithelium segmentation was evaluated with Intersection Over Union (IOU). (c) The performance of nucleus detection was assessed by correlating U-Net counting with manual counting across 16 small tissue regions.