Improving cell tracking by jointly handling missed detections, false detections, cell divisions and higher-order motion models

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In fields ranging from developmental to cell biology, very high cell tracking accuracy is required to gain fundamental insight from the quantitative analysis of cell trajectories. However, the required precision is not yet achieved by any existing cell tracking algorithm. In this project, we plan to increase the expressiveness of so-called tracking-by-assignment models while decreasing their runtime. To achieve the former, we will introduce factors that are higher order in time to model prior knowledge such as constant velocity, smoothness of trajectories and consistency with respect to the cell cycle. These developments need to be tightly coupled with improvements in approximate combinatorial optimization. Together, these developments should expand the state of the art in computer vision so as to finally allow a reliable cell tracking in challenging 2D+time and 3D+time datasets from the life sciences.