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Verfasst von:Godinez, William J. [VerfasserIn]   i
 Lampe, Marko [VerfasserIn]   i
 Koch, Peter [VerfasserIn]   i
 Eils, Roland [VerfasserIn]   i
 Müller, Barbara [VerfasserIn]   i
 Rohr, Karl [VerfasserIn]   i
Titel:Identifying virus-cell fusion in two-channel fluorescence microscopy image sequences based on a layered probabilistic approach
Verf.angabe:William J. Godinez* (student member, IEEE), Marko Lampe, Peter Koch, Roland Eils, Barbara Müller, and Karl Rohr, (member, IEEE)
Umfang:23 S.
Fussnoten:Gesehen am 05.09.2018
Titel Quelle:Enthalten in: Institute of Electrical and Electronics Engineers: IEEE transactions on medical imaging
Jahr Quelle:2012
Band/Heft Quelle:31(2012), 9, S. 1786-1808
ISSN Quelle:1558-254X
Abstract:The entry process of virus particles into cells is decisive for infection. In this work, we investigate fusion of virus particles with the cell membrane via time-lapse fluorescence microscopy. To automatically identify fusion for single particles based on their intensity over time, we have developed a layered probabilistic approach. The approach decomposes the action of a single particle into three abstractions: the intensity over time, the underlying temporal intensity model, as well as a high level behavior. Each abstraction corresponds to a layer and these layers are represented via stochastic hybrid systems and hidden Markov models. We use a maxbelief strategy to efficiently combine both representations. To compute estimates for the abstractions we use a hybrid particle filter and the Viterbi algorithm. Based on synthetic image sequences, we characterize the performance of the approach as a function of the image noise. We also characterize the performance as a function ofthe tracking error. We have also successfully applied the approach to real image sequences displaying pseudotyped HIV-1 particles in contact with host cells and compared the experimental results with ground truth obtained by manual analysis.
DOI:doi:10.1109/TMI.2012.2203142
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Verlag: http://dx.doi.org/10.1109/TMI.2012.2203142
 Verlag: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6213119&tag=1
 DOI: https://doi.org/10.1109/TMI.2012.2203142
Datenträger:Online-Ressource
Sprache:eng
K10plus-PPN:1580689507
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