Automated multi-dimensional, high-content image-analysis for fluorescence-microscopy (A04)

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For the research of host-pathogen interactions fluores¬cence microscopy has been established itself as an essential evaluation tool. To improve and objectify related experiments based on complex fluorescent image data imagery, image processing and analysis methods approaches are used. Specifically, in order to segment and analyze complex multi-modal fluorescent imagery, new extended image analysis procedures will be addressed, developed and evaluated, based on so-called convolutional neural networks (CNNs) as well as deep learning approaches. These approaches will be applied and validated on various tasks from the CRC.