CellCognition Project
Initial releaseDecember 2009; 14 years ago (2009-12)
Stable release
1.6.1 / May 1, 2017; 6 years ago (2017-05-01)
Operating systemAny (Python based)
TypeImage processing & Computer vision & Machine Learning
LicenseLGPL license
Websitewww.cellcognition-project.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]

Features

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.[3]

The software is written in Python 2.7 and binaries are available for Windows and Mac OS X.

History

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 latest release is 1.6.1 and the software is developed and maintained by the Gerlich Lab at the Institute of Molecular Biotechnology.

Application

CellCognition has been used in RNAi-based screening,[4] applied in basic cell cycle study,[5] and extended to unsupervised modeling.[6]

References

  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" (PDF). Nature Methods. 7 (9): 747–54. doi:10.1038/nmeth.1486. hdl:1912/3869. PMID 20693996. S2CID 205419414.
  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. PMC 3839080. PMID 20711181.
  3. ^ Zhong, Qing; Busetto, Alberto Giovanni; Fededa, Juan P.; Buhmann, Joachim M.; Gerlich, Daniel W. (July 2012). "Unsupervised modeling of cell morphology dynamics for time-lapse microscopy". Nature Methods. 9 (7): 711–713. doi:10.1038/nmeth.2046. hdl:11336/21074. ISSN 1548-7105. PMID 22635062. S2CID 1962889.
  4. ^ 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 3018799. PMID 21113133.
  5. ^ 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". Journal of Cell Biology. 198 (2): 173–83. doi:10.1083/jcb.201112112. PMC 3410419. PMID 22801782.
  6. ^ 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. hdl:11336/21074. PMID 22635062. S2CID 1962889.

External links

Image processing software
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