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Probabilistic Multi-Target Tracking for Biological Systems

Automated image based tracking is widely used to investigate collective interactions in a variety of biological systems. Applied to diverse environments, ranging from cell to animal colonies, it provides insight into biological processes like cancer metastasis, immunological response, and social structure of animals. However, the majority of existing tracking systems is highly specific and not capable of analyzing scenarios with a highly structured or dynamic background. Here, we present a modular tracking toolbox for analyzing large image datasets of varying complexity. It can be customized to provide tracking algorithms suitable for the respective problem and existing modules for segmentation, filtering, and tracking can easily be implemented. As a proof-of-concept, the software is used to analyze collective motion behavior of adelié penguins in Antarctica, disturbance of sea birds due to wind turbines in the North Sea, and motility of T cells in an extracellular matrix. Automated data analysis results in more than 90% correct coverage compared to manually acquired tracks. Methods for object recognition are adapted to the individual system, whereas track assignment is found to be sufficiently accurate with the same algorithm, Kalman filtering. Ultimately, the presented software toolbox provides a solution for image based tracking of a variety of biological systems. The expandable modules provide high flexibility and obviate the need for dedicated software development for every single problem. This approach is considered a starting point for future investigations on image data from even more complex scenarios, such as collective movement in emperor penguin colonies and territorial behavior of breeding adelié penguins.

We will provide a link to our software shortly (after all bugs have been removed)