Deterministic and probabilistic approaches for tracking virus particles in time-lapse fluorescence microscopy image sequences

Godinez, William J.; Lampe, Marko; Wörz, Stefan; Müller, Bárbara; Eils, Roland; Rohr, Karl

doi:10.1016/j.media.2008.12.004

Cited by 96 publications

(131 citation statements)

References 37 publications

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“…However, the development of a tracking algorithm which delivers high-quality results is a challenging task. Low contrast of the images, auto fluorescent background, high object density, complex motion patterns and other aspects complicate the tracking task [4].…”

Section: Introductionmentioning

confidence: 99%

Comparing human and algorithmic tracking of subviral particles in fluorescence microscopic image sequences

Müller

Rausch

Dolnik

et al. 2017

Current Directions in Biomedical Engineering

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Abstract:Tracking of subviral particles with automated methods enables the analysis of intracellular processes exhibited by viruses. A linear assignment problem solver and a Kalman-filter have been added to an existing particle tracking algorithm. First results produced with simulated image sequences showed that the improved algorithm is able to improve tracking results by closing gaps in the particle's trajectories. Here we report on the evaluation of the LAPKalman algorithm using real fluorescence-microscopic images. The results from the original and improved algorithm have been compared to the results of manual tracking. Evaluation results indicate that the improved algorithm is capable to reconstruct missing parts of particle tracks in difficult conditions. However, the evaluation of the algorithms and the manual tracking is a complex task because of the low image contrast and high object density with intersecting tracks in the live-cell images.

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Section: Introductionmentioning

confidence: 99%

Comparing human and algorithmic tracking of subviral particles in fluorescence microscopic image sequences

Müller

Rausch

Dolnik

et al. 2017

Current Directions in Biomedical Engineering

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show abstract

“…Sequential Monte Carlo (SMC) methods are typically applied in cases of nonlinearity and nonGaussian statistics, in particular particle filtering (PF). In [3] a mixture of PFs was used for tracking microtubule growth in fluorescence confocal microscopy. PFs are again employed in [3], for tracking virus particles in timelapse fluorescence microscopy.…”

Section: Introductionmentioning

confidence: 99%

“…In [3] a mixture of PFs was used for tracking microtubule growth in fluorescence confocal microscopy. PFs are again employed in [3], for tracking virus particles in timelapse fluorescence microscopy. Detailed analysis was done, comparing a mixture of PFs, independent PFs, and other deterministic approaches.…”

Section: Introductionmentioning

confidence: 99%

“…Detailed analysis was done, comparing a mixture of PFs, independent PFs, and other deterministic approaches. In their analysis [3] demonstrated the superior performance of independent PFs, for tracking a large number of targets.…”

Section: Introductionmentioning

confidence: 99%

“…Details of our RJMCMC tracking algorithm are presented in Section. 3. Experimental results are shown in Section.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

RJMCMC-based tracking of vesicles in fluorescence time-lapse microscopy

Nam

Arkill²,

Eales

et al. 2015

2015 23rd European Signal Processing Conference (EUSIPCO)

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Untangling cell tracks: Quantifying cell migration by time lapse image data analysis

et al. 2017

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Automated microscopy has given researchers access to great amounts of live cell imaging data from in vitro and in vivo experiments. Much focus has been put on extracting cell tracks from such data using a plethora of segmentation and tracking algorithms, but further analysis is normally required to draw biologically relevant conclusions. Such relevant conclusions may be whether the migration is directed or not, whether the population has homogeneous or heterogeneous migration patterns. This review focuses on the analysis of cell migration data that are extracted from time lapse images. We discuss a range of measures and models used to analyze cell tracks independent of the biological system or the way the tracks were obtained. For single-cell migration, we focus on measures and models giving examples of biological systems where they have been applied, for example, migration of bacteria, fibroblasts, and immune cells. For collective migration, we describe the model systems wound healing, neural crest migration, and Drosophila gastrulation and discuss methods for cell migration within these systems. We also discuss the role of the extracellular matrix and subsequent differences between track analysis in vitro and in vivo. Besides methods and measures, we are putting special focus on the need for openly available data and code, as well as a lack of common vocabulary in cell track analysis. © 2017 International Society for Advancement of Cytometry.

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Deterministic and probabilistic approaches for tracking virus particles in time-lapse fluorescence microscopy image sequences

Cited by 96 publications

References 37 publications

Comparing human and algorithmic tracking of subviral particles in fluorescence microscopic image sequences

Comparing human and algorithmic tracking of subviral particles in fluorescence microscopic image sequences

RJMCMC-based tracking of vesicles in fluorescence time-lapse microscopy

Untangling cell tracks: Quantifying cell migration by time lapse image data analysis

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