2026/04/24 by Zimeng Wu, Octavian Voiculescu, Alessandro Mongera +3 · 1 voice
Biochemistry, Genetics and Molecular Biology · #Cell Image Analysis Techniques #Zebrafish Biomedical Research Applications #Advanced Fluorescence Microscopy Techniques
paper · pdf · doi:10.1242/jcs.264728
Advances in microscopy and bioimage analysis are enabling unprecedented quantitative observation of dynamic biological systems. Smart microscopy closes the loop by feeding back image-derived information to control image acquisition on the fly, paving the way for increasingly autonomous and sophisticated experiments. However, adoption of smart microscopy remains limited primarily to specialists, and even simple tasks such as live tracking of moving samples are still widely handled manually. Here, we describe DySTrack, a modular open-source Python tool that serves as a minimal bridge between commercial acquisition software and arbitrary image analysis pipelines, allowing users to stick with familiar vendor-developed user interfaces for microscope configuration while leveraging the powerful and platform-agnostic Python ecosystem for image analysis. DySTrack comes with detailed documentation and with ready-to-use, easy-to-adapt example pipelines that track moving tissues (namely the zebrafish lateral line primordium and the chick Hensen's node) during embryonic development. We hope DySTrack will contribute to a recent push to make smart microscopy more widely accessible.