![]() Figure labels: RH (“RemoveHoles”), Close (“Closing”), Erode (“Erosion”), Mask (“MaskImage”), Math (“ImageMath”), EorS Features (“EnhanceOrSuppressFeatures”). (B) CellProfiler 3.0 image processing modules used for membrane image processing. (A) Original 3D image of blastocyst cell membrane prior to analysis. Images were captured of a mouse embryo blastocyst cell membrane stained with WGA and FISH for GAPDH transcripts. Segmentation steps for the quantification of transcripts per cell within a 3D blastocyst. 3D, three-dimensional hiPSC, human induced pluripotent stem cell. Object accuracy comparisons of these same images may be found in S6 Fig and S3 File. (E) Comparison of the segmentation accuracy of CellProfiler 3.0 and Fiji’s plugin MorphoLibJ, based on the Rand index of the processed image and its ground truth (out of a total of 1.0). More information about segmentation steps used for these images can be found in S2– S5 Figs. Raw images (left) and CellProfiler outputs (right) showing nuclei of mouse embryo blastocyst (A), mouse trophoblast stem cells (B), and synthetic images of HL60 cell lines (C) and (D). 3D, three-dimensional hiPSC, human induced pluripotent stem cell.Įxamples of 3D image segmentation produced by CellProfiler 3.0, across two experimental systems and two sets of synthesized images. (E) Image processing done using Fiji’s MorphoLibJ plugin (macro code is presented in S1 Table). (D) Ground truth obtained by manual annotation of each Z-slice using GIMP software. Figure labels: RH (“RemoveHoles”), EorS Features (“EnhanceOrSuppressFeatures”). (C) Selected CellProfiler 3.0 image processing modules used for hiPSC nucleus segmentation. Both were compared to manually annotated ground truth using CellProfiler’s MeasureImageOverlap module. (B) Evaluation of CellProfiler 3.0 performance in comparison to the MorphoLibJ plugin in Fiji software. (A) Original 3D image of nuclei monolayer prior to analysis. ![]() Images are from the Allen Institute for Cell Science, Seattle, and available from the Broad Bioimage Benchmark Collection ( ). Let us know if you encounter a bug by submitting a GitHub issue.Volumetric processing for 3D images of DNA-stained nuclei of hiPSCs using CellProfiler 3.0. You can download a beta release for macOS and Windows from the CellProfiler website. If you’re an enthusiastic CellProfiler user, you should try the beta release of CellProfiler. Let us know if we’ve inadvertently broken your module by submitting a GitHub issue. You can download a nightly release for macOS and Windows from the CellProfiler website. If you’re the maintainer of a third-party CellProfiler module, you should use the nightly release of CellProfiler. ![]() Instructions for compiling CellProfiler on Linux, macOS and Windows are available from CellProfiler’s GitHub wiki. If you’re contributing or planning to contribute to CellProfiler, you should compile CellProfiler from source. You can download a stable release for macOS and Windows from the CellProfiler website. ![]() We recommend the stable release of CellProfiler. What version of CellProfiler should I use? More information can be found in the CellProfiler Wiki. CellProfiler is a free open-source software designed to enable biologists without training in computer vision or programming to quantitatively measure phenotypes from thousands of images automatically. ![]()
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