A system architecture for online data interpretation and reduction in fluorescence microscopy

In this paper we present a high-throughput sample screening system that enables real-time data analysis and reduction for live cell analysis using fluorescence microscopy. We propose a novel system architecture capable of analyzing a large amount of samples during the experiment and thus greatly minimizing the post-analysis phase that is the common practice today. By utilizing data reduction algorithms, relevant information of the target cells is extracted from the online collected data stream, and then used to adjust the experiment parameters in real-time, allowing the system to dynamically react on changing sample properties and to control the microscope setup accordingly. The proposed system consists of an integrated DSP-FPGA hybrid solution to ensure the required real-time constraints, to execute efficiently the underlying computer vision algorithms and to close the perception-action loop. We demonstrate our approach by addressing the selective imaging of cells with a particular combination of markers. With this novel closed-loop system the amount of superfluous collected data is minimized, while at the same time the information entropy increases.