Efficient Acquisition and Learning of Fluorescence Microscope Data Models

We present a method for efficient acquisition of fluorescence microscope datasets, to allow for higher spatial and temporal resolution, and with less damage from photobleaching. Our proposal is to restrict acquisition to regions where we expect to find an object. Given that the objects are continuously moving, we must have an accurate model to describe objects´ motion to predict their future locations. We outline a system for learning and applying this motion model, provide details from some simple simulations, and summarize results from more complex applications.