An automated cell-tracking pipeline for the analysis of neutrophil dynamics.

Neutrophils play a key role in the innate immune system. They act as the primary line of defense when bacteria, viruses, or other harmful foreign particles invade the immune system. Accurate movement measurement of neutrophils, including velocity, direction, and displacement, is crucial to studying the regulation of cell migration behavior. Cell tracking is a key technology to realize the quantification of these measurements. In this article, we developed a pipeline, including cell segmentation, cell motion tracking between two frames, and trajectory linkage, to realize cell tracking. Our starting point was to collect time-lapse sequences of neutrophils using a confocal microscope. We pre-processed each frame in the time-lapse sequence to improve the image quality by denoising, smoothing, and contrast enhancement. Subsequently, a deep learning model, that is, U-Net, was used to segment cells in each image frame. U-Net was used again to track the cells between two adjacent frames by calculating the score matrices representing the posterior probability of linkage. Moreover, an extended Viterbi algorithm was applied to find optimal trajectories based on score matrices generated by the U-Net. Results demonstrate that our pipeline outperforms other representative linkage methods used in cell tracking. It provides a robust, practical solution for a challenging and highly motile