A team of scientists at Heidelberg University and the German Cancer Research Center (also in Heidelberg, Germany) has developed a powerful particle analysis technique for live cell microscopy images. This so-called probabilistic particle tracking method is automatic, computer-based, and can be used for time-resolved two- and three-dimensional (2D and 3D) microscopy image data.
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Automatically tracking the movement of biological particles such as viruses, cell vesicles, or cell receptors is of key importance in biomedical applications for quantitative analysis of intracellular dynamic processes. But manually analyzing time-resolved microscopy images with hundreds or thousands of moving objects is not feasible. Recognizing this, the research team—including Dr. William J. Godinez and associate professor Dr. Karl Rohr of the Biomedical Computer Vision group at Heidelberg's BioQuant Center—developed a particle tracking technique that is based on a mathematically sound method from probability theory that takes into account uncertainties in the image data (such as noise) and exploits knowledge of the application domain.
"Compared to deterministic methods, our probabilistic approach achieves high accuracy, especially for complicated image data with a large number of objects, high object density, and a high level of noise," says Rohr. The method enables determining the movement paths of objects and quantifies relevant parameters such as speed, path length, motion type, or object size. In addition, important dynamic events such as virus-cell fusions are detected automatically.
Full details of the research team's work appear in the journal Nature Methods; for more information, please visit http://dx.doi.org/10.1038/nmeth.2808.
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