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Tracking virus particles in fluorescence microscopy images using multi-scale detection and multi-frame association

Automatic fluorescent particle tracking is an essential task to study the dynamics of a large number of biological structures at a sub-cellular level. We have developed a probabilistic particle tracking approach based on multi-scale detection and two-step multi-frame association. The multi-scale det...

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Bibliographic Details
Main Authors: Jaiswal, Astha (Author) , Godinez, William J. (Author) , Eils, Roland (Author) , Lehmann, Maik J. (Author) , Rohr, Karl (Author)
Format: Article (Journal)
Language:English
Published: 17 July 2015
In: IEEE transactions on image processing
Year: 2015, Volume: 24, Issue: 11, Pages: 4122-4136
ISSN:1941-0042
DOI:10.1109/TIP.2015.2458174
Online Access:Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1109/TIP.2015.2458174
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Author Notes:Astha Jaiswal, Member IEEE, William J. Godinez, Member IEEE, Roland Eils, Maik Jörg Lehmann, and Karl Rohr
Description
Summary:Automatic fluorescent particle tracking is an essential task to study the dynamics of a large number of biological structures at a sub-cellular level. We have developed a probabilistic particle tracking approach based on multi-scale detection and two-step multi-frame association. The multi-scale detection scheme allows coping with particles in close proximity. For finding associations, we have developed a two-step multi-frame algorithm, which is based on a temporally semiglobal formulation as well as spatially local and global optimization. In the first step, reliable associations are determined for each particle individually in local neighborhoods. In the second step, the global spatial information over multiple frames is exploited jointly to determine optimal associations. The multi-scale detection scheme and the multi-frame association finding algorithm have been combined with a probabilistic tracking approach based on the Kalman filter. We have successfully applied our probabilistic tracking approach to synthetic as well as real microscopy image sequences of virus particles and quantified the performance. We found that the proposed approach outperforms previous approaches.
Item Description:Gesehen am 23.06.2020
Physical Description:Online Resource
ISSN:1941-0042
DOI:10.1109/TIP.2015.2458174

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