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. 2021 Oct 7;10:210. doi: 10.1038/s41377-021-00649-9

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© The Author(s) 2021

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. 4 — a, b 3D visualizations of emulated shell-scan and propagative-scan (β = 5) using raw data from a full scan acquisition. c Goodness of the shell estimated as a function of the size of the pre-scan. The goodness is measured from the percentage of epithelial bright voxels (i.e. bright voxels that are in the ground truth shell) that fall in the estimated shell (the objective being to maximize this quantity). The result is shown with and without outlier removal in the surface estimation step. d Goodness of cell contour delineation with the shell-scan and propagative-scan, as a function of the number of acquisitions required. For the propagative-scan, results with different NN maximal prediction distances β are shown (β = 1; 3; 5; 10). All points plotted on these curves correspond to the average of the result over 50 different random pre-scans (with error bars in (c) and the height and width of ellipses in (d) set to their standard-deviation)