![]() The increasing use of automated microscopy now allows researchers to capture images of samples treated with many thousands of individual compounds or genetic perturbations. Image analysis software can allow scientists to obtain quantitative measurements from images that are otherwise difficult to capture via subjective observation. ![]() Microscopy can be used to capture images which contain a wealth of information that can inform biomedical research. ![]() 2 are provided in a public GitHub repository ( ). These adjusted pipelines as well as the sample data and pipeline used to produce Fig. The pipeline for this data set was originally written for CellProfiler 2, and so was adjusted to run on CellProfiler 3 and CellProfiler 4 with comparable outputs. 5b, b,6, 6, and Additional file 1: Figure S1 made use of a previously published data set (Plate 37983 from ) and pipeline (analysis.cppipe from, cited in ). Cell Painting benchmarking experiments in Figs. 1a, a,3d, 3d, and and4a 4a were performed with the publicly available “3D Monolayer” pipeline and image set which can be accessed at. Benchmarking and visualizations presented in Figs. 1c, d, f, f,3a–c, 3a–c, 4b, c, and and5a 5a was performed with the publicly available pipelines and image set “ExampleFly” ( )). Pre-compiled builds for Windows and MacOS, as well as documentation manuals, are available at. ![]() GUID: D87956E2-BC6A-4FCE-AF77-4E9676337162 Data Availability StatementĬellProfiler 4 is open-source software which has been made freely available to the scientific community. The Creative Commons Public Domain Dedication waiver ( ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. If material is not included in the article's Creative Commons licence 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. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. MCT.Open AccessThis 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 licence, and indicate if changes were made. For sparse data RobustBackground makes sense, and the idea of merging it with Background is nice.Īnd perhaps one of the other methods, e.g. Otsu is widely used and the three class option makes it flexible. I have made a pull-request with my implementation and it would be really nice if that could be included in CellProfiler. Ridler-Calvard, Otsu, MCT, Kapur among the methods in CellProfiler), and some that worked well for the Spots (unfortunately none of the CellProfiler methods), but our method was the only one that worked well for all datasets. There were several methods that worked well for the cell images (e.g. We compared our global thresholding method (Size Interval Precision, SIP), which is based on object features instead of just histogram features, to all these other thresholding methods. 385-390.įluorescent Cell Nuclei, Cell Clusters + 2 types of synthetic spots (mimicing images of sparse fluorescent spots on noisy background). We did a comparison of all global threshold methods in CellProfiler and also in ImageJ in our paper “Global Gray-level Thresholding Based on Object Size”, Petter Ranefall and Carolina Wählby, Cytometry Part A, 89:4, 2016, pp. Then being able to insert those variables with the same right click method as for metadata in the saving modules. So, while I wouldn’t touch IdentifyPrimaryObjects itself, what I’d love to see is user defined variables, in a module at the start, where they can be easily identified and commented or better in a CSV file, being able to specify different settings for different cell lines (which also serves as a record of the parameters used for each). In any case, tuning IdentifyPrimaryObjects is the most time consuming part of the process, especially when different cell lines are used. My least used ones are Kapur, MoG and Ridler Calvard. RobustBackground is very good and well argued for by Christian. It seems to me that Otsu should stay, even if few final projects would actually use it, because it’s one of the most famous and best understood methods and people would use it as a benchmark for other methods.
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