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A Novel Pipeline for Cell Instance Segmentation, Tracking and Motility Classification of Toxoplasma Gondii in 3D Space

Seyed Alireza Vaezi
University of Georgia

Gianni Orlando
University of Georgia

Mojtaba Fazli
harvard University

Gary Ward
University of Vermont

Silvia Moreno
University of Georgia

Shannon Quinn
University of Georgia


Toxoplasma gondii is the parasitic protozoan that causes disseminated toxoplasmosis, a disease that is estimated to infect around one-third of the world's population. While the disease is commonly asymptomatic, the success of the parasite is in large part due to its ability to easily spread through nucleated cells. The virulence of T. gondii is predicated on the parasite's motility. Thus the inspection of motility patterns during its lytic cycle has become a topic of keen interest. Current cell tracking projects usually focus on cell images captured in 2D which are not a true representation of the actual motion of a cell. Current 3D tracking projects lack a comprehensive pipeline covering all phases of preprocessing, cell detection, cell instance segmentation, tracking, and motion classification, and merely implement a subset of the phases. Moreover, current 3D segmentation and tracking pipelines are not targeted for users with less experience in deep learning packages. Our pipeline, TSeg, on the other hand, is developed for segmenting, tracking, and classifying the motility phenotypes of T. gondii in 3D microscopic images. Although TSeg is built initially focusing on T. gondii, it provides generic functions to allow users with similar but distinct applications to use it off-the-shelf. Interacting with all of TSeg's modules is possible through our Napari plugin which is developed mainly off the familiar SciPy scientific stack. Additionally, our plugin is designed with a user-friendly GUI in Napari which adds several benefits to each step of the pipeline such as visualization and representation in 3D. TSeg proves to fulfill a better generalization, making it capable of delivering accurate results with images of other cell types.




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