| Initial release | December 2009; 15 years ago (2009-12) |
|---|---|
| Stable release | 1.6.1 / May 1, 2017; 7 years ago (2017-05-01) |
| Operating system | Any (Python based) |
| Type | Image processing &Computer vision &Machine Learning |
| License | LGPL license |
| Website | www |
CellCognition is a freeopen-source computational framework for quantitative analysis of high-throughputfluorescence microscopy (time-lapse) images in the field ofbioimage informatics and systems microscopy. The CellCognition framework usesimage processing,computer vision andmachine 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 ofphenotype map.[1][2]
CellCognition uses a computational pipeline which includesimage segmentation,object detection,feature extraction,statistical classification,tracking of individual cells over time, detection of class-transition motifs (e.g. cells entering mitosis), andHMM 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.
CellCognition (Version 1.0.1) was first released in December 2009 by scientists from the Gerlich Lab and the Buhmann group at theSwiss Federal Institute of Technology Zürich and the Ellenberg Lab at theEuropean Molecular Biology Laboratory Heidelberg. The latest release is 1.6.1 and the software is developed and maintained by the Gerlich Lab at theInstitute of Molecular Biotechnology.
CellCognition has been used inRNAi-based screening,[4] applied in basiccell cycle study,[5] and extended to unsupervised modeling.[6]
