Uncertainty-aware Tracking of Single Bacteria over Image Sequences with Low Frame Rate
Abstract: In single-cell analysis, the physiologic states of individual cells are studied. In some studies, the subject of interest is the development over time of some cell characteristic. To obtain time-resolved single-cell data, one possibility is to conduct an experiment on a cell population and make a sequence of images of the population over the course of the experiment. If a mapping is at hand, which determines which cell it is that is the cause of each measured cell in the image sequence, time resolved single-cell data can be extracted. Such a mapping is called a lineage tree, and the process of creating it is called tracking. One aim of this work is to develop a tracking algorithm that incorporates organism specific knowledge, such as average division time, in the tracking process. With respect to this aim, a Bayesian model that incorporates biological knowledge is derived, with which every hypothetical lineage tree can be assigned a probability. Additionally, two Monte Carlo algorithms are developed, that approximate the probability distribution of lineage trees given by the Bayesian model. When an approximate distribution is known, for example the most likely lineage tree can be extracted and used. In many cases, the information provided to an automatic tracking algorithm is insufficient for the algorithm to find the gold standard lineage tree. In these cases, a possibility is to construct the gold standard lineage tree by manual correction of the lineage tree that has been provided by the tracking algorithm. A second aim of this work is to provide a confidence to every assignment in a lineage tree, in order to give the person doing manual corrections useful information about what assignments to change. Such a confidence is provided by the Monte Carlo tracking methods developed in this work.
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