Rapid detection and classification of motile cell tracks in 3D

Tracks of motile microbes can be used to identify species, such as pathogens, with different swimming behaviours. They provide detailed information on responses to external stimuli such as chemical gradients and physical objects. Digital holographic microscopy (DHM) is a well-established, but computationally intensive method for obtaining three-dimensional cell tracks from video microscopy data. We use DHM data as ground truth libraries for a deep learning object detection network. The trained network allows a 100-fold increase in processing speed, and is suitable for implementation in real-time applications on modest computing hardware. Furthermore, we explore a range of machine learning tools for track classification and discuss potential applications in species identification and life detection.