Objective comparison of particle tracking methods

By Nicolas Chenouard, Ihor Smal, F. de Chaumont, Martin Maška, Ivo F. Sbalzarini, Yuanhao Gong, Janick Cardinale, C. Carthel, S. Coraluppi, Mark R. Winter and 25 more. Nature Methods, 2014

892 citations recorded by the source corpus.

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.

Subfields: Biophysics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/5d580ebfbfb426a8c4f9a6678e5d5802b2f22b59

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Citations in the corpus

Metrics Reloaded: Recommendations for image analysis validation (2022, background): “[22] Nicolas Chenouard, Ihor Smal, Fabrice De Chaumont, Martin Maška, Ivo F Sbalzarini, Yuanhao Gong, Janick Cardinale, Craig Carthel, Stefano Coraluppi, Mark Winter, et al.”
Crowdsourcing the creation of image segmentation algorithms for connectomics (2015, methodology): “Crowdsourcing has also been employed to drive innovation in scientiﬁc problems, such as biological sequence analysis (Lakhani et al., 2013) and particle tracking in microscopy images (Chenouard et al., 2014).”
TrackMate 7: integrating state-of-the-art segmentation algorithms into tracking pipelines (2022, background): “1 and Supplementary Video 6).”
An Objective Comparison of Cell Tracking Algorithms (2017, neutral): “The conclusion that algorithms that use prior and contextual information perform better than those that do not use it was also reached in the aforementioned Particle Tracking Challenge 34 .”
Single-Cell and Single-Molecule Analysis of Gene Expression Regulation (2016, background): “Thus, various tracking algorithms have been developed to find the most probable set of particle trajectories (31).”
The ImageJ ecosystem: Open‐source software for image visualization, processing, and analysis (2020, background): “29,67,68 Objects or structures of interest are separated from the background signal and then can be tracked frame-to-frame.”