![]() ![]() © 2018 International Society for Advancement of Cytometry. CellProfiler allowed rapid high processing capacity of chromogenically stained chronic inflammatory tissue that was reliable, accurate, and reproducible and highlights potential applications in research diagnostics.ĬellProfiler chromogenic image analysis immunohistochemistry oral mucosa. CellProfiler demonstrated versatility with the ability to assess large numbers of images and allowed additional parameters to be quantified. Furthermore, CellProfiler allowed the determination of multiple variables simultaneously, such as area stained and masking to remove any nonstained tissue and white gaps, which also demonstrated reliable agreement (r 2 = >0.9). Scatterplots and Bland-Altman plots demonstrated strong agreement between the manual counters and CellProfiler, with the number of positively stained cells robustly correlating (r 2 = 0.938). Validation comparisons between the manual counters demonstrated strong intra-observer concordance (r 2 = 0.979), particularly when cell numbers were less than 100. Inter-observer and inter-platform agreements were assessed by scatterplots with linear regression and Bland-Altman plots. Digitized images were manually counted and subjected to image analysis in CellProfiler. Oral mucosal biopsies from patients with chronic graft-versus-host disease were stained for CD4. The current investigation aimed to compare CellProfiler quantitative chromogenic IHC analyses against the gold standard manual counting. The open-source digital analysis software, CellProfiler has been extensively used for fluorescently stained cells/tissues however, chromogenic IHC staining is routinely used in both pathological and research diagnostics. We also post guided exercises as part of our educational outreach effort.Visual grading of chromogenically stained immunohistochemical (IHC) samples is subjective, time consuming, and predisposed to considerable inter- and intra-observer variations. Simple nuclei identification tutorial ( sample data) (courtesy of the German BioImaging network) Performing a colocalization assay ( relevant example pipeline) Using the Worm Toolbox for image analysis of C. This maximum possible value is defined by the Set intensity range from setting in NamesAndTypes see the help for that setting for more details. Identifying and measuring cells: Cytoplasm-nucleus translocation assay ( relevant example pipeline)Ĭalculating and applying illumination correction for images ( relevant example pipeline) Keep in mind that the default behavior in CellProfiler is to rescale the image intensity from 0 to 1 by dividing all pixels in the image by the maximum possible intensity value. Explore your data and classify complex or subtle phenotypes using machine learning in CellProfiler Analyst. Adjust the settings to measure the phenotypes of interest in your images. Identifying, measuring, and classifying yeast colonies ( relevant example pipeline) Designed for biologists Load an example CellProfiler pipeline, a series of image-processing modules. Using the Input modules in CellProfiler 2.1: Using CellProfiler for Quantitative Image Analysis The NIH has published a introductory chapter of “best practices” for image-based high-content screening (in which CellProfiler is mentioned) as part of the Assay Guidance Manual, and our group has published a more advanced follow-up chapter on image analysis methods. Our introduction to automated image analysis principles and practicalities is published as an educational article at PLoS. Technical descriptions of CellProfiler and CellProfiler Analyst software can be found in our papers while more written tutorials can be found on the CellProfiler GitHub page. Visit our YouTube playlist for video tutorials on CellProfiler, CellProfiler Analyst, segmentation strategies, how to construct pipelines, and much more. ![]()
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