An open-source microscopy framework for simultaneous control of image acquisition, reconstruction, and analysis

• Published in: HardwareX Vol. 13; p. e00400
• Main Authors: Casas Moreno, Xavier, Silva, Mariline Mendes, Roos, Johannes, Pennacchietti, Francesca, Norlin, Nils, Testa, Ilaria
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.03.2023; Elsevier
• Subjects: Automation; Computer and Information Science; Computer Engineering; Data- och informationsvetenskap (Datateknik); Datorteknik; Natural Sciences; Naturvetenskap; RESOLFT; Software; Automation; Software; RESOLFT
• ISSN: 2468-0672; 2468-0672
• DOI: 10.1016/j.ohx.2023.e00400

[Display omitted] We present a computational framework to simultaneously perform image acquisition, reconstruction, and analysis in the context of open-source microscopy automation. The setup features multiple computer units intersecting software with hardware devices and achieves automation using python scripts. In practice, script files are executed in the acquisition computer and can perform any experiment by modifying the state of the hardware devices and accessing experimental data. The presented framework achieves concurrency by using multiple instances of ImSwitch and napari working simultaneously. ImSwitch is a flexible and modular open-source software package for microscope control, and napari is a multidimensional image viewer for scientific image analysis. The presented framework implements a system based on file watching, where multiple units monitor a filesystem that acts as the synchronization primitive. The proposed solution is valid for any microscope setup, supporting various biological applications. The only necessary element is a shared filesystem, common in any standard laboratory, even in resource-constrained settings. The file watcher functionality in Python can be easily integrated into other python-based software. We demonstrate the proposed solution by performing tiling experiments using the molecular nanoscale live imaging with sectioning ability (MoNaLISA) microscope, a high-throughput super-resolution microscope based on reversible saturable optical fluorescence transitions (RESOLFT).