Feature point tracking and trajectory analysis for video imaging in cell biology.

By Ivo F. Sbalzarini, Petros Koumoutsakos. Journal of Structural Biology, 2005

1,483 citations recorded by the source corpus.

This paper presents a computationally efficient, two-dimensional, feature point tracking algorithm for the automated detection and quantitative analysis of particle trajectories as recorded by video imaging in cell biology. The tracking process requires no a priori mathematical modeling of the motion, it is self-initializing, it discriminates spurious detections, and it can handle temporary occlusion as well as particle appearance and disappearance from the image region. The efficiency of the algorithm is validated on synthetic video data where it is compared to existing methods and its accuracy and precision are assessed for a wide range of signal-to-noise ratios. The algorithm is well suited for video imaging in cell biology relying on low-intensity fluorescence microscopy. Its applicability is demonstrated in three case studies involving transport of low-density lipoproteins in endosomes, motion of fluorescently labeled Adenovirus-2 particles along microtubules, and tracking of quantum dots on the plasma membrane of live cells. The present automated tracking process enables the quantification of dispersive processes in cell biology using techniques such as moment scaling spectra.

Subfields: Biophysics, Immunology

View Paper: https://www.semanticscholar.org/paper/cf0b02e7868bfa2aeffb605ee5328f8f25c1e5f6

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Citations in the corpus

SINGLE-PARTICLE TRACKING (2009, background): “A pioneering work in the area is by Crocker and Grier [48] (for later developments see [84– 86]).”
Objective comparison of particle tracking methods (2014, background): “Each page shows the results for one density level (Low, Mid, High) and the four different SNR levels (1, 2, 4, 7).”
How do you find the green sheep? A critical review of the use of remotely sensed imagery to detect and count animals (2018, background): “…small animals that may have been missed due to the small number of pixels they occupy, or by exaggerating differences F IGURE 1 Particle Tracker (Sbalzarini & Koumoutsakos, 2005) in ImageJ, designed for video imaging in cell biology, to detect sheep: (a) original image of farming area in NZ at…”
Jamming during particle spreading in additive manufacturing (2018, methodology): “[32] I.F. Sbalzarini, P. Koumoutsakos, Feature point tracking and trajectory analysis for video imaging in cell biology, J. Struct.”
Automated and semi‐automated cell tracking: addressing portability challenges (2011, methodology): “Some of the methods (Kirubarajan et al. , 2001; Sbalzarini & Koumoutsakos, 2005; Chowdhury et al. , 2010) do not directly handle the cases of cell divisions and deaths, hence requiring an adaptation for the cell tracking problem.”
Coupled minimum-cost flow cell tracking for high-throughput quantitative analysis (2011, methodology): “Challenge Contour Stochastic Segment & Evolution Filtering Associate Mitosis & Merging + Appearing & Disappearing Low Temporal Resolution + + Fast Motion + + Tracking Drift + Accurate Segmentation + + Selected References [6, 7, 8, 9] [10, 11, 12, 13] [14, 15, 16, 17]”
Methods for cell and particle tracking. (2012, background): “, 2005) and graph-based optimization approaches (Sbalzarini and Koumoutsakos, 2005; Jaqaman et al., 2008).”
Tracking in cell and developmental biology. (2009, background): “An alternative (but related) approach is to construct a so-called weighted graph (or a cost matrix) from the detected particles and possible correspondences (between two or more frames) with their likelihoods, and to find the optimal subgraph (corresponding to the set of trajectories) by minimizing ”