Joint segmentation detection and tracking
We have developed a novel method for joint segmentation, detection and tracking of multiple objects. The method is based on a probabilistic model that is defined implicitly in terms of a Markov chain Monte Carlo algorithm. The parameters of the model are learned using an objective based on empirical risk minimization (Sixta et al., 2020). Our method was used by researchers from the Cells-in-Motion (CiM) Cluster of Excellence at the University of Münster for analysing the molecular mechanisms of motion and contact dynamics of endothelial cells when they form new blood vessels (full story). This work led to a joint publication in Nature Communications (Cao et al., 2017).
Papers
- Sixta, T., Cao, J., Seebach, J., Schnittler, H., & Flach, B. (2020). Coupling cell detection and tracking by temporal feedback. Machine Vision and Applications, 31(4). https://doi.org/10.1007/s00138-020-01072-7
- Cao, J., Ehling, M., März, S., Seebach, J., Tarbashevich, K., Sixta, T., Pitulescu, M. E., Werner, A. C., Flach, B., Montanez, E., Raz, E., Adams, R. H., & Schnittler, H. (2017). Polarized actin and VE-cadherin dynamics regulate junctional remodelling and cell migration during sprouting angiogenesis. Nature Communications, 8(1), 2210–. https://doi.org/10.1038/s41467-017-02373-8