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Objective comparison of particle tracking methods

Autores: Chenouard, N. ; Smal, I. ; de Chaumont, F. ; Maška, M.; Sbalzarini, I. F. ; Gong, Y. ; Cardinale, J. ; Carthel, C.; Coraluppi, S. ; Winter, M. ; Cohen, A. R. ; Godinez, W. J. ; Rohr, K.; Kalaidzidis, Y. ; Liang, L. ; Duncan, J. ; Shen, H. ; Xu, Y. ; Magnusson, K. E.; Jaldén, J.; Blau, H. M.; Paul-Gilloteaux, P. ; Roudot, P. ; Kervrann, C. ; Waharte, F. ; Tinevez, J. Y. ; Shorte, S. L. ; Willemse, J. ; Celler, K. ; van Wezel, G. P. ; Dan, H. W. ; Tsai, Y. S. ; Ortiz de Solórzano Aurusa, Carlos; Olivo-Marin, J. C. ; Meijering, E.
Título de la revista: NATURE METHODS
ISSN: 1548-7091
Volumen: 11
Número: 3
Páginas: 281-289
Fecha de publicación: 2014
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.