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In the biomedical environment, experiments assessing dynamic processes areprimarily performed by a human acquisition supervisor. Contemporaryimplementations of such experiments frequently aim to acquire a maximum numberof relevant events from sometimes several hundred parallel, non-synchronousprocesses. Since in some high-throughput experiments, only one or a fewinstances of a given process can be observed simultaneously, a strategy forplanning and executing an efficient acquisition paradigm is essential. Toaddress this problem, we present two new methods in this paper. The firstmethod, Encoded Dynamic Process (EDP), is Artificial Intelligence (AI)-basedand represents dynamic processes so as to allow prediction of pseudo-timevalues from single still images. Second, with Experiment Automation Pipelinefor Dynamic Processes (EAPDP), we present a Machine Learning Operations(MLOps)-based pipeline that uses the extracted knowledge from EDP toefficiently schedule acquisition in biomedical experiments for dynamicprocesses in practice. In a first experiment, we show that the pre-trainedState-Of-The- Art (SOTA) object segmentation method Contour Proposal Networks(CPN) works reliably as a module of EAPDP to extract the relevant object forEDP from the acquired three-dimensional image stack.

AI-based automated active learning for discovery of hidden dynamic processes: A use case in light microscopy