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Developer(s) Micheal Held, Christoph Sommer, Daniel Gerlich, Qing Zhong, Bernd Fischer, Thomas Walter, Jan Ellenberg, and others (IMBA, ETHZ, EMBL Heidelberg)
Initial release December 2009; 7 years ago (2009-12)
Stable release
1.2.5 / January 3, 2012; 5 years ago (2012-01-03)
Operating system Any (Python based)
Type Image processing & Computer vision & Machine Learning
License LGPL license
Website www.cellcognition.org

CellCognition is a free open-source computational framework for quantitative analysis of high-throughput fluorescence microscopy (time-lapse) images in the field of bioimage informatics and systems microscopy. The CellCognition framework uses image processing, computer vision and machine learning techniques for single-cell tracking and classification of cell morphologies. This enables measurements of temporal progression of cell phases, modeling of cellular dynamics and generation of phenotype map.[1][2]


CellCognition uses a computational pipeline which includes image segmentation, object detection, feature extraction, statistical classification, tracking of individual cells over time, detection of class-transition motifs (e.g. cells entering mitosis), and HMM correction of classification errors on class labels.

The software is a cross-platform application and runs on the three major operating systems (Microsoft Windows, Mac OS X, and Linux). It combines VIGRA based C++ computer vision library with Python based workflow engine and graphical user interface.


CellCognition (Version 1.0.1) was first released in December 2009 by scientists from the Gerlich Lab and the Buhmann group at the Swiss Federal Institute of Technology Zürich and the Ellenberg Lab at the European Molecular Biology Laboratory Heidelberg. The current version is 1.2.5 and being developed and maintained by the Gerlich Lab at the Institute of Molecular Biotechnology.


CellCognition has been used in RNAi-based screening,[3] applied in basic cell cycle study,[4] and extended to unsupervised modeling.[5]


  1. ^ Held M, Schmitz MH, Fischer B, Walter T, Neumann B, Olma MH, Peter M, Ellenberg J, Gerlich DW (2010). "CellCognition: time-resolved phenotype annotation in high-throughput live cell imaging". Nature Methods. 7 (9): 747–54. doi:10.1038/nmeth.1486. PMID 20693996. 
  2. ^ Schmitz MH, Held M, Janssens V, Hutchins JR, Hudecz O, Ivanova E, Goris J, Trinkle-Mulcahy L, Lamond AI, Poser I, Hyman AA, Mechtler K, Peters JM, Gerlich DW (2010). "Live-cell imaging RNAi screen identifies PP2A-B55alpha and importin-beta1 as key mitotic exit regulators in human cells". Nature Cell Biology. 12 (9): 886–93. doi:10.1038/ncb2092. PMID 20711181. 
  3. ^ Piwko W, Olma MH, Held M, Bianco JN, Pedrioli PG, Hofmann K, Pasero P, Gerlich DW, Peter M (2010). "RNAi-based screening identifies the Mms22L-Nfkbil2 complex as a novel regulator of DNA replication in human cells". EMBO Journal. 29 (24): 4210–22. doi:10.1038/emboj.2010.304. PMC 3018799Freely accessible. PMID 21113133. 
  4. ^ Wurzenberger C, Held M, Lampson MA, Poser I, Hyman AA, Gerlich DW (2012). "Sds22 and Repo-Man stabilize chromosome segregation by counteracting Aurora B on anaphase kinetochores". EMBO Journal. 198 (2): 173–83. doi:10.1083/jcb.201112112. PMC 3410419Freely accessible. PMID 22801782. 
  5. ^ Zhong Q, Busetto AG, Fededa JP, Buhmann JM, Gerlich DW (2012). "Unsupervised modeling of cell morphology dynamics for time-lapse microscopy". Nature Methods. 9 (7): 711–13. doi:10.1038/nmeth.2046. PMID 22635062. 

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