A Real-Time Automated Deep Learning Workflow for Non-Invasive High-Magnification Imaging of C. elegans

Caenorhabditis elegans is a premier model organism for aging and neurobiology research, valued for its short lifespan, optical transparency, genetic tractability, and well-mapped nervous system. Automated imaging without immobilization is an important goal because it reduces reliance on anesthesia, restraint, and repeated handling. High-magnification imaging of freely moving worms remains challenging. Increasing magnification narrows the field of view, leading to target loss, motion blur, and focal drift. Immobilization approaches can alter physiology, suppress natural behavior, and limit longitudinal observation. Here, we present a fully automated real-time tracking workflow for imaging individual worms in a microfluidic platform under controlled culture conditions. The system integrates deep-learning-based head detection, image-based autofocus, and rapid motorized-stage feedback to support high-magnification imaging of freely moving worms. Tracking and image acquisition use the same microscope and objective. The platform maintains individually housed worms in a microfluidic environment while preserving animal identity for repeated imaging over time. Built entirely from commercially available equipment and requiring no additional custom tracking hardware, the platform provides modular, user-configurable tracking parameters that can be adapted to specific experimental goals.