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Tracking cell particles in 3D microscopy videos is a challenging task but isof great significance for modeling the motion of cells. Proper characterizationof the cell's shape, evolution, and their movement over time is crucial tounderstanding and modeling the mechanobiology of cell migration in manydiseases. One in particular, toxoplasmosis is the disease caused by theparasite Toxoplasma gondii. Roughly, one-third of the world's population testspositive for T. gondii. Its virulence is linked to its lytic cycle, predicatedon its motility and ability to enter and exit nucleated cells; therefore,studies elucidating its motility patterns are critical to the eventualdevelopment of therapeutic strategies. Here, we present a computationalframework for fast and scalable detection, tracking, and identification of T.gondii motion phenotypes in 3D videos, in a completely unsupervised fashion.Our pipeline consists of several different modules including preprocessing,sparsification, cell detection, cell tracking, trajectories extraction,parametrization of the trajectories; and finally, a clustering step.Additionally, we identified the computational bottlenecks, and developed alightweight and highly scalable pipeline through a combination of taskdistribution and parallelism. Our results prove both the accuracy andperformance of our method.

Lightweight and Scalable Particle Tracking and Motion Clustering of 3D Cell Trajectories