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Automated cell boundary and 3D nuclear segmentation of cells in suspension

dc.contributor.authorKesler, Benjamin
dc.contributor.authorLi, Guoliang
dc.contributor.authorThiemicke, Alexander
dc.contributor.authorVenkat, Rohit
dc.contributor.authorNeuert, Gregor
dc.identifier.citationKesler, B., Li, G., Thiemicke, A., Venkat, R., & Neuert, G. (2019). Automated cell boundary and 3D nuclear segmentation of cells in suspension. Scientific reports, 9(1), 10237.
dc.description.abstractTo characterize cell types, cellular functions and intracellular processes, an understanding of the differences between individual cells is required. Although microscopy approaches have made tremendous progress in imaging cells in different contexts, the analysis of these imaging data sets is a long-standing, unsolved problem. The few robust cell segmentation approaches that exist often rely on multiple cellular markers and complex time-consuming image analysis. Recently developed deep learning approaches can address some of these challenges, but they require tremendous amounts of data and well-curated reference data sets for algorithm training. We propose an alternative experimental and computational approach, called CellDissect, in which we first optimize specimen preparation and data acquisition prior to image processing to generate high quality images that are easier to analyze computationally. By focusing on fixed suspension and dissociated adherent cells, CellDissect relies only on widefield images to identify cell boundaries and nuclear staining to automatically segment cells in two dimensions and nuclei in three dimensions. This segmentation can be performed on a desktop computer or a computing cluster for higher throughput. We compare and evaluate the accuracy of different nuclear segmentation approaches against manual expert cell segmentation for different cell lines acquired with different imaging modalities.en_US
dc.description.sponsorshipB.K., G.L., A.T., R.V. and G.N. are supported by NIH DP2 GM11484901, NIH R01GM115892, and Vanderbilt Startup Funds. Additionally, B.K. was supported by T32GM008320, RV was supported by T32LM012412 and A.T. is supported by an AHA predoctoral fellowship (18PRE34050016). Vanderbilt ACCRE computing cluster is supported by NIH S10OD023680. We would like to thank J.T. Lee for sharing mESC 16.7 cell line and K. Gould for sharing S. pombe 972h strain.en_US
dc.publisherScientific Reportsen_US
dc.rightsOpen 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 © The Author(s) 2019
dc.titleAutomated cell boundary and 3D nuclear segmentation of cells in suspensionen_US

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