Synthesis and Modeling of Volumetric Cell Images for Enhanced Training of Machine Learning Classifiers – An Application for Selective Plane Illumination Microscopy (SPIM) - (Project Academy “Engineering Sciences” DFG-Call No. 56)

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Biomedical research is demanding more and more on developments in microscopy technology to enable quantitative analysis of cell and tissue growth. Established 3D fluorescence microscopy technologies like confocal microscopy are not able to achieve these requirements due to a lack of penetration depth or photo toxic effects prohibiting the long term monitoring of living tissue. Selective Plane Illumination Microscopy (SPIM) offers a solution for this kind of problems. Automated sample positioning and image acquisition are essential for reproducibility, acquisition rate and high throughput. One goal is the implementation of machine learning algorithms and a cascaded classifier to achieve a fast and robust image analysis. The main goal is to realise of a closed loop feedback system both for sample positioning and image acquisition.