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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| Hauptverfasser: | , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
06 June 2012
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| In: |
IEEE transactions on medical imaging
Year: 2012, Jahrgang: 31, Heft: 9, Pages: 1786-1808 |
| ISSN: | 1558-254X |
| DOI: | 10.1109/TMI.2012.2203142 |
| Online-Zugang: | 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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| Verfasserangaben: | William J. Godinez* (student member, IEEE), Marko Lampe, Peter Koch, Roland Eils, Barbara Müller, and Karl Rohr, (member, IEEE) |
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| 245 | 1 | 0 | |a Identifying virus-cell fusion in two-channel fluorescence microscopy image sequences based on a layered probabilistic approach |c William J. Godinez* (student member, IEEE), Marko Lampe, Peter Koch, Roland Eils, Barbara Müller, and Karl Rohr, (member, IEEE) |
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| 520 | |a 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. | ||
| 650 | 4 | |a Algorithms | |
| 650 | 4 | |a Approximation methods | |
| 650 | 4 | |a Bayes Theorem | |
| 650 | 4 | |a Behavior identification | |
| 650 | 4 | |a biomedical imaging | |
| 650 | 4 | |a biomedical optical imaging | |
| 650 | 4 | |a biomembranes | |
| 650 | 4 | |a Cell Fusion | |
| 650 | 4 | |a cell membrane | |
| 650 | 4 | |a Cell Tracking | |
| 650 | 4 | |a Cells (biology) | |
| 650 | 4 | |a cellular biophysics | |
| 650 | 4 | |a Computational modeling | |
| 650 | 4 | |a diseases | |
| 650 | 4 | |a fluorescence | |
| 650 | 4 | |a HeLa Cells | |
| 650 | 4 | |a hidden Markov models | |
| 650 | 4 | |a Hidden Markov models | |
| 650 | 4 | |a HIV-1 | |
| 650 | 4 | |a Host-Pathogen Interactions | |
| 650 | 4 | |a Humans | |
| 650 | 4 | |a hybrid particle filter | |
| 650 | 4 | |a image denoising | |
| 650 | 4 | |a image noise | |
| 650 | 4 | |a image sequences | |
| 650 | 4 | |a infection | |
| 650 | 4 | |a layered probabilistic approach | |
| 650 | 4 | |a Markov Chains | |
| 650 | 4 | |a maxbelief strategy | |
| 650 | 4 | |a maximum likelihood estimation | |
| 650 | 4 | |a medical image processing | |
| 650 | 4 | |a microorganisms | |
| 650 | 4 | |a Microscopy | |
| 650 | 4 | |a microscopy images | |
| 650 | 4 | |a Microscopy, Fluorescence | |
| 650 | 4 | |a Models, Biological | |
| 650 | 4 | |a optical microscopy | |
| 650 | 4 | |a particle filtering (numerical methods) | |
| 650 | 4 | |a Predictive models | |
| 650 | 4 | |a probability | |
| 650 | 4 | |a pseudotyped HIV-1 particles | |
| 650 | 4 | |a stochastic hybrid systems | |
| 650 | 4 | |a Stochastic processes | |
| 650 | 4 | |a Stochastic Processes | |
| 650 | 4 | |a synthetic image sequences | |
| 650 | 4 | |a temporal intensity model | |
| 650 | 4 | |a time-lapse ίuorescence microscopy | |
| 650 | 4 | |a tracking | |
| 650 | 4 | |a tracking error function | |
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