CellProfiler: image analysis software for identifying and quantifying cell phenotypes. Biologists can now prepare and image thousands of samples per day using automation, enabling chemical screens and functional genomics (for example, using RNA interference). Here we describe the first free, open-source system designed for flexible, high-throughput cell image analysis, CellProfiler. CellProfiler can address a variety of biological questions quantitatively, including standard assays (for example, cell count, size, per-cell protein levels) and complex morphological assays (for example, cell/organelle shape or subcellular patterns of DNA or protein staining).

References in zbMATH (referenced in 17 articles )

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  1. Tiger Cross; Rasika Navarange; Joon-Ho Son; William Burr; Arjun Singh; Kelvin Zhang; Miruna Rusu; Konstantinos Gkoutzis; Andrew Osborne; Bart Nieuwenhuis: Simple RGC: ImageJ Plugins for Counting Retinal Ganglion Cells and Determining the Transduction Efficiency of Viral Vectors in Retinal Wholemounts (2021) not zbMATH
  2. Berry, Eric; Chen, Yen-Chi; Cisewski-Kehe, Jessi; Fasy, Brittany Terese: Functional summaries of persistence diagrams (2020)
  3. Joshua C. Peterson, Marco C. DeRuiter: Fluorescent Nuclei Measurements Macro (FNMM), a tool for automated cell quantification in ImageJ (2020) not zbMATH
  4. Öfverstedt, Johan; Lindblad, Joakim; Sladoje, Nataša: Stochastic distance transform: theory, algorithms and applications (2020)
  5. Ruman Gerst; Anna Medyukhina; Marc Thilo Figge: MISA++: A standardized interface for automated bioimage analysis (2020) not zbMATH
  6. Bühlmann, Peter: Comments on “Data science, big data and statistics” (2019)
  7. Li, Qiwei; Wang, Xinlei; Liang, Faming; Xiao, Guanghua: A Bayesian mark interaction model for analysis of tumor pathology images (2019)
  8. Shah, Nihar B.; Wainwright, Martin J.: Simple, robust and optimal ranking from pairwise comparisons (2018)
  9. Curtis T. Rueden, Johannes Schindelin, Mark C. Hiner, Barry E. DeZonia, Alison E. Walter, Kevin W. Eliceiri: ImageJ2: ImageJ for the next generation of scientific image data (2017) arXiv
  10. Khan, Arif Ul Maula; Mikut, Ralf; Schweitzer, Brigitte; Weiss, Carsten; Reischl, Markus: Automatic tuning of image segmentation parameters by means of fuzzy feature evaluation (2013) ioport
  11. Kamentsky, Lee; Jones, Thouis R.; Fraser, Adam; Bray, Mark-Anthony; Logan, David J.; Madden, Katherine L.; Ljosa, Vebjorn; Rueden, Curtis; Eliceiri, Kevin W.; Carpenter, Anne E.: Improved structure, function and compatibility for cellprofiler: modular high-throughput image analysis software (2011) ioport
  12. Aydin, Zafer; Murray, John I.; Waterston, Robert H.; Noble, William Stafford: Using machine learning to speed up manual image annotation: application to a 3D imaging protocol for measuring single cell gene expression in the developing \textitC. Elegans embryo (2010) ioport
  13. Ivanovska, Tetyana; Schenk, Andrea; Homeyer, André; Deng, Meihong; Dahmen, Uta; Dirsch, Olaf; Hahn, Horst K.; Linsen, Lars: A fast and robust hepatocyte quantification algorithm including vein processing (2010) ioport
  14. Lawless, Conor; Wilkinson, Darren J.; Young, Alexander; Addinall, Stephen G.; Lydall, David A.: Colonyzer: automated quantification of micro-organism growth characteristics on solid agar (2010) ioport
  15. Misselwitz, Benjamin; Strittmatter, Gerhard; Periaswamy, Balamurugan; Schlumberger, Markus C.; Rout, Samuel; Horvath, Peter; Kozak, Karol; Hardt, Wolf-Dietrich: Enhanced cellclassifier: a multi-class classification tool for microscopy images (2010) ioport
  16. Ruusuvuori, Pekka; Äijö, Tarmo; Chowdhury, Sharif; Garmendia-Torres, Cecilia; Selinummi, Jyrki; Birbaumer, Mirko; Dudley, Aimée M.; Pelkmans, Lucas; Yli-Harja, Olli: Evaluation of methods for detection of fluorescence labeled subcellular objects in microscope images (2010) ioport
  17. Gebäck, Tobias; Koumoutsakos, Petros: Edge detection in microscopy images using curvelets (2009) ioport