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Description:
This thesis presents a novel approach for tracking a varying number of divisible objects with similar appearance in the presence of a non-negligible number of false positive detections (more than 10%). It is applied to the reconstruction of cell lineages in developing zebrafish and fruit fly embryos from 3d time-lapse record- ings. The model takes the form of a chain graph—a mixed directed-undirected probabilistic graphical model—and a tracking is obtained simultaneously over all time slices from the maximum a-posteriori configuration. The tracking model is used as the second step in a two-step pipeline to produce digital embryos—maps of cell nuclei in an embryo and their ancestral fate; the first step being the segmentation of the fluorescently-stained cell nuclei in light sheet microscopy images. The pipeline is implemented as a software with an intuitive graphical user interface. It is the first freely available program of its kind and makes the presented methods accessible to a broad audience of users from the life sciences.