Plant cell tracking using Kalman filter based local graph matching

摘要

Automated tracking of cells in time lapse live-imaging datasets of developing multicellular tissues is required for high throughput spatio-temporal quantitative measurements of a range of cell behaviors, such as cell division, migration and cell growth. In this paper, a Kalman filter based local graph matching method is proposed to track the plant cells, by exploiting the tight spatial topology of neighboring cells in a multicellular field as contextual information. The Kalman filter is used to predict the movement of the cells, and then the local graph matching approach is used to search the target cells in the neighborhood of the predicted position. The combination of the Kalman filter and local graph matching greatly reduces the size of the searching region in the matching process and enhances the tracking stability as well. Furthermore, the cells' lineage tracklets could be associated by using the cells' spatial-temporal contextual information to obtain long-term lineages. Finally, we proposed a graph evolution method to enhance the association robustness by considering the statistical properties of individual cell tracklets. The effectiveness and efficiency of the proposed tracking method are validated by experiments on real datasets. (C) 2016 Elsevier B.V. All rights reserved.