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The CellCognition framework is designed to combine object detection and supervised machine learning for classification of morphologies with time-resolved analysis by single-cell tracking. This enables measurements of progression through morphology stages and kinetic readouts at the single-cell level.


The software was designed to run on the three major platforms (Windows, Mac OSX, Linux) at high speed with high convenience. We therefore combined C++ image processing based on VIGRA with a Python-based workflow engine and a Qt-based graphical user-interface.





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