From Wikipedia, the free encyclopedia
Jump to navigation Jump to search
CellProfiler logo 2017.png
Developer(s)Anne E. Carpenter, Thouis Jones, Lee Kamentsky, Allen Goodman, Claire McQuin, and others (Broad Institute)
Stable release
3.0.0 (revision eb6fa7a) / October 2017; 1 year ago (2017-10)
Repository Edit this at Wikidata
Operating systemAny (Python-based)
TypeImage processing & Image analysis
LicenseBSD 3-clause

CellProfiler[1][2] is free, open-source software designed to enable biologists without training in computer vision or programming to quantitatively measure phenotypes from thousands of images automatically. Advanced algorithms for image analysis are available as individual modules that can be placed in sequential order together to form a pipeline; the pipeline is then used to identify and measure biological objects and features in images, particularly those obtained through fluorescence microscopy.

Distributions are available for Microsoft Windows, macOS, and Linux. The source code for CellProfiler is freely available[3]. CellProfiler is developed by the Broad Institute's Imaging Platform.[4]


CellProfiler can read and analyze most common microscopy image formats.[5] Biologists typically use CellProfiler to identify objects of interest (e.g. cells, colonies, C. elegans worms) and then measure their properties of interest.[6] Specialized modules for illumination correction may be applied as pre-processing step to remove distortions due to uneven lighting.[7] Object identification (segmentation) is performed through machine learning or image thresholding, recognition and division of clumped objects, and removal or merging of objects on the basis of size or shape.[8] Each of these steps are customizable by the user for their unique image assay.

A wide variety of measurements can be generated for each identified cell or subcellular compartment, including morphology, intensity, and texture among others. These measurements are accessible by using built-in viewing and plotting data tools, exporting in a comma-delimited spreadsheet format,[9] or importing into a MySQL or SQLite database.[10]

CellProfiler interfaces with the high-performance scientific libraries NumPy and SciPy for many mathematical operations, the Open Microscopy Environment[11] Consortium’s Bio-Formats library for reading more than 100 image file formats, ImageJ for use of plugins and macros, and ilastik for pixel-based classification.[12] While designed and optimized for large numbers of two-dimensional images (the most common high-content screening image format), CellProfiler supports analysis of small-scale experiments and time-lapse movies.[13]


CellProfiler was released in December 2005 by scientists from the Whitehead Institute for Biomedical Research and Massachusetts Institute of Technology.[14] It is currently developed and maintained by the Carpenter Lab at the Imaging Platform of the Broad Institute.[15]

Originally developed in MATLAB,[14] it was re-written in Python and released as CellProfiler 2.0 in 2010.[2] Version 3.0, supporting volumetric analysis of 3D image stacks and optional deep learning modules, was released in October 2017.[16]


Because CellProfiler is a free, open-source project, anyone can develop their own image processing algorithms as a new module for CellProfiler and contribute it to the project.[17] The CellProfiler website contains a forum for discussion where new users can have their questions answered, usually by the creators of the project.[18]


  1. ^ Carpenter AE, Jones TR, Lamprecht MR, Clarke C, Kang IH, Friman O, Guertin DA, Chang JH, Lindquist RA, Moffat J, Golland P, Sabatini DM (2006). "CellProfiler: image analysis software for identifying and quantifying cell phenotypes". Genome Biology. 7 (10): R100. doi:10.1186/gb-2006-7-10-r100. PMC 1794559. PMID 17076895.
  2. ^ a b Kamentsky L, Jones TR, Fraser A, Bray MA, Logan DJ, Madden KL, Ljosa V, Rueden C, Eliceiri KW, Carpenter AE (April 2011). "Improved structure, function and compatibility for CellProfiler: modular high-throughput image analysis software". Bioinformatics. 27 (8): 1179–80. doi:10.1093/bioinformatics/btr095. PMC 3072555. PMID 21349861.
  3. ^ "CellProfiler wiki". GitHub. December 2016.
  4. ^ "Imaging Platform". Broad Institute. 2018.
  5. ^ "CellProfiler — Bio-Formats 5.2.1 documentation". www.openmicroscopy.org. Retrieved 2016-08-29.
  6. ^ Lamprecht, Michael R.; Sabatini, David M.; Carpenter, Anne E. (2007-01-01). "CellProfiler: free, versatile software for automated biological image analysis". BioTechniques. 42 (1): 71–75. doi:10.2144/000112257. ISSN 0736-6205. PMID 17269487.
  7. ^ Singh, S.; Bray, M.-A.; Jones, T. R.; Carpenter, A. E. (2014-12-01). "Pipeline for illumination correction of images for high-throughput microscopy". Journal of Microscopy. 256 (3): 231–236. doi:10.1111/jmi.12178. ISSN 1365-2818. PMC 4359755. PMID 25228240.
  8. ^ "IdentifyPrimaryObjects". d1zymp9ayga15t.cloudfront.net. Retrieved 2016-08-29.
  9. ^ "ExportToSpreadsheet". d1zymp9ayga15t.cloudfront.net. Retrieved 2016-08-29.
  10. ^ "ExportToDatabase". d1zymp9ayga15t.cloudfront.net. Retrieved 2016-08-29.
  11. ^ "Open Microscopy Environment". Retrieved 2018-05-07.
  12. ^ "CellProfiler/CellProfiler". GitHub. Retrieved 2016-08-29.
  13. ^ Bray, Mark-Anthony; Carpenter, Anne E. (2015-01-01). "CellProfiler Tracer: exploring and validating high-throughput, time-lapse microscopy image data". BMC Bioinformatics. 16: 368. doi:10.1186/s12859-015-0759-x. ISSN 1471-2105. PMC 4634901. PMID 26537300.
  14. ^ a b "What Is the Key Best Practice for Collaborating with a Computational Biologist?". Cell Systems. 3 (1): 7–11. 2016. doi:10.1016/j.cels.2016.07.006. PMID 27467242.
  15. ^ "The Carpenter Lab". 2018.
  16. ^ Carpenter, AE (2017-10-16). "CellProfiler 3.0 release: faster, better, and 3D". CellProfiler Blog.
  17. ^ "CellProfiler/CellProfiler". GitHub. Retrieved 2016-08-29.
  18. ^ "CellProfiler". forum.cellprofiler.org. Retrieved 2016-08-29.

External links[edit]