Detection and tracking of migrating oligodendrocyte progenitor cells from in vivo fluorescence time-lapse imaging data

In this work, we develop a fully automatic algorithm named "MCDT" (Migrating Cell Detector and Tracker) for the integrated task of migrating cell detection, segmentation and tracking from in vivo fluorescence time-lapse microscopy imaging data. The interest of detecting and tracking migrating cells arouses from the scientific question in understanding the impact of oligodendrocyte progenitor cells (OPCs) migration in vivo, using advanced microscopy imaging techniques. Current practice of OPC mobility analysis relies on manual labeling, suffering from massive human labor, subjective biases, and weak reproducibility. Existing cell tracking methods have difficulties in analyzing such challenging data due to the extra complexity of in vivo data. Designed for in vivo data, MCDT circumvents the common strong assumption of separable feature distributions between foreground and background. Besides, by focusing on migrating cells (OPCs) only, MCDT relieves the burden of tracking all irrelevant cells correctly, not only accelerating the analysis but also achieving better accuracy in OPCs. Seed based segmentation and tracking by topology-preserved motion estimation endows MCDT with robustness to complex surroundings of the cell under tracking and to occasional inaccurate segmentation in some frames. We tested MCDT on imaging data of transgenic zebrafish larval spinal cord and MCDT showed very promising performance.