Identifying virus-cell fusion in two-channel fluorescence microscopy image sequences based on a layered probabilistic approach
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...
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| Main Authors: | , , , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
06 June 2012
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| In: |
IEEE transactions on medical imaging
Year: 2012, Volume: 31, Issue: 9, Pages: 1786-1808 |
| ISSN: | 1558-254X |
| DOI: | 10.1109/TMI.2012.2203142 |
| Online Access: | Verlag, Volltext: http://dx.doi.org/10.1109/TMI.2012.2203142 Verlag, Volltext: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6213119&tag=1 
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| Author Notes: | William J. Godinez* (student member, IEEE), Marko Lampe, Peter Koch, Roland Eils, Barbara Müller, and Karl Rohr, (member, IEEE) |
| Summary: | 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 of the 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. |
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| Item Description: | Gesehen am 05.09.2018 |
| Physical Description: | Online Resource |
| ISSN: | 1558-254X |
| DOI: | 10.1109/TMI.2012.2203142 |