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CellCognition
Software for microscopy From Wikipedia, the free encyclopedia
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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]
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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.
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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
External links
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