Identifying virus-cell fusion in two-channel fluorescence microscopy image sequences based on a layered probabilistic approach

• Verfasst von: Godinez, William J. [VerfasserIn]
• Verfasst von: Lampe, Marko [VerfasserIn]
• Verfasst von: Koch, Peter [VerfasserIn]
• Verfasst von: Eils, Roland [VerfasserIn]
• Verfasst von: Müller, Barbara [VerfasserIn]
• Verfasst von: Rohr, Karl [VerfasserIn]
• 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
• DOI: doi:10.1109/TMI.2012.2203142
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 1580689507

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.