Nicolas Chenouard 1 , 2 , 3 , Ihor Smal 4 , 5 , Fabrice de Chaumont 1 , Martin Maška 6 , 7 , Ivo F Sbalzarini 8 , Yuanhao Gong 8 , Janick Cardinale 8 , Craig Carthel 9 , Stefano Coraluppi 9 , Mark Winter 10 , Andrew R Cohen 10 , William J Godinez 11 , 12 , Karl Rohr 11 , 12 , Yannis Kalaidzidis 13 , 14 , Liang Liang 15 , James Duncan 15 , Hongying Shen 16 , Yingke Xu 17 , Klas E G Magnusson 18 , Joakim Jaldén 18 , Helen M Blau 19 , Perrine Paul-Gilloteaux 20 , Philippe Roudot 21 , Charles Kervrann 21 , François Waharte 20 , Jean-Yves Tinevez 22 , Spencer L Shorte 22 , Joost Willemse 23 , Katherine Celler 23 , Gilles P van Wezel 23 , Han-Wei Dan 24 , Yuh-Show Tsai 24 , Carlos Ortiz de Solórzano 6 , Jean-Christophe Olivo-Marin , 1 , Erik Meijering , 4 , 5
19 January 2014
The first community competition designed to objectively compare the performance of particle tracking algorithms provides valuable practical information for both users and developers.
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.