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Objective comparison of particle tracking methods.

MPS-Authors

Chenouard,  Nicolas
Max Planck Society;

/persons/resource/persons219674

Smal,  Ihor
Max Planck Institute of Molecular Cell Biology and Genetics, Max Planck Society;

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Sbalzarini,  Ivo F.
Max Planck Institute of Molecular Cell Biology and Genetics, Max Planck Society;

/persons/resource/persons219192

Gong,  Yuanhao
Max Planck Institute of Molecular Cell Biology and Genetics, Max Planck Society;

/persons/resource/persons219054

Cardinale,  Janick
Max Planck Institute of Molecular Cell Biology and Genetics, Max Planck Society;

Carthel,  Craig
Max Planck Society;

Coraluppi,  Stefano
Max Planck Society;

Winter,  Mark
Max Planck Society;

Godinez,  William J.
Max Planck Society;

Rohr,  Karl
Max Planck Society;

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Kalaidzidis,  Yannis
Max Planck Institute of Molecular Cell Biology and Genetics, Max Planck Society;

Liang,  Liang
Max Planck Society;

Duncan,  James
Max Planck Society;

Shen,  Hongying
Max Planck Society;

Xu,  Yingke
Max Planck Society;

Magnusson,  Klas E. G.
Max Planck Society;

Blau,  Helen M.
Max Planck Society;

Paul-Gilloteaux,  Perrine
Max Planck Society;

Roudot,  Philippe
Max Planck Society;

Kervrann,  Charles
Max Planck Society;

Waharte,  Francois
Max Planck Society;

/persons/resource/persons219737

Tinevez,  Jean-Yves
Max Planck Institute of Molecular Cell Biology and Genetics, Max Planck Society;

Willemse,  Joost
Max Planck Society;

Celler,  Katherine
Max Planck Society;

Dan,  Han-Wei
Max Planck Society;

Tsai,  Yuh-Show
Max Planck Society;

Olivo-Marin,  Jean-Christophe
Max Planck Society;

Meijering,  Erik
Max Planck Society;

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Citation

Chenouard, N., Smal, I., Chaumont, F. d., Maška, M., Sbalzarini, I. F., Gong, Y., et al. (2014). Objective comparison of particle tracking methods. Nature Methods, 11(3), 281-289.


Cite as: https://hdl.handle.net/21.11116/0000-0001-05D1-D
Abstract
Particle tracking is of key importance for quantitative analysis of intracellular dynamic processes from time-lapse microscopy image data. Because manually detecting and following large numbers of individual particles is not feasible, automated computational methods have been developed for these tasks by many groups. Aiming to perform an objective comparison of methods, we gathered the community and organized an open competition in which participating teams applied their own methods independently to a commonly defined data set including diverse scenarios. Performance was assessed using commonly defined measures. Although no single method performed best across all scenarios, the results revealed clear differences between the various approaches, leading to notable practical conclusions for users and developers.