“Smart” or adaptive feedback microscopy or event driven microscopy aims to combine bioimage analysis (and other algorithms) with fully motorized and computer-controlled microscopes to create automated and adaptive measurement sequences. This enables the user for example to monitor transient and rare events in the desired level of detail across a large cell population or over long observation times or to streamline the optimization of acquisition parameters and the batch analysis of multiple measurements in a single script. Here, we present the development of a scripting concept based on the Python programming language for the fully motorized single-photon counting confocal microscope Luminosa . We illustrate these benefits by presenting specific use cases for automated optimization of acquisition parameters. The framework fully opens up the microscope interface to allow the user to call external python libraries without compromising the workflows and functionalities present in the microscope software, such as the user settings, laser safety interlock, and predefined functionality blocks for auto-alignment, live views, etc. Thus scripting demands for the user remain rather low enabling non-experts to develop their own measurement/analysis pipelines. We envision that the community will build upon this python interface bringing the rapidly evolving open source activities closer to the commercial instrumentation allowing for more efficient method developments and granting end users direct access to cutting-edge developments.
Hagen et al. (Sun,) studied this question.