TrackMate: An open and extensible platform for single-particle tracking.

By J. Tinevez, N. Perry, Johannes E. Schindelin, Genevieve M Hoopes, G. D. Reynolds, E. Laplantine, S. Bednarek, S. Shorte, Kevin W. Eliceiri. Methods, 2017

3,057 citations recorded by the source corpus.

We present TrackMate, an open source Fiji plugin for the automated, semi-automated, and manual tracking of single-particles. It offers a versatile and modular solution that works out of the box for end users, through a simple and intuitive user interface. It is also easily scriptable and adaptable, operating equally well on 1D over time, 2D over time, 3D over time, or other single and multi-channel image variants. TrackMate provides several visualization and analysis tools that aid in assessing the relevance of results. The utility of TrackMate is further enhanced through its ability to be readily customized to meet specific tracking problems. TrackMate is an extensible platform where developers can easily write their own detection, particle linking, visualization or analysis algorithms within the TrackMate environment. This evolving framework provides researchers with the opportunity to quickly develop and optimize new algorithms based on existing TrackMate modules without the need of having to write de novo user interfaces, including visualization, analysis and exporting tools. The current capabilities of TrackMate are presented in the context of three different biological problems. First, we perform Caenorhabditis-elegans lineage analysis to assess how light-induced damage during imaging impairs its early development. Our TrackMate-based lineage analysis indicates the lack of a cell-specific light-sensitive mechanism. Second, we investigate the recruitment of NEMO (NF-κB essential modulator) clusters in fibroblasts after stimulation by the cytokine IL-1 and show that photodamage can generate artifacts in the shape of TrackMate characterized movements that confuse motility analysis. Finally, we validate the use of TrackMate for quantitative lifetime analysis of clathrin-mediated endocytosis in plant cells.

Subfields: Biophysics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/21b1f408e158932e911e1bb512d74ac13095a2a2

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Citations in the corpus

TrackMate 7: integrating state-of-the-art segmentation algorithms into tracking pipelines (2022, neutral): “1, and Supplementary Video 3).”
The ImageJ ecosystem: Open‐source software for image visualization, processing, and analysis (2020, methodology): “Users are provided with algorithmic results for tracking, as well as quantitative results and plots.(29,70) TrackMate includes several tracking algorithms, such as LAP tracking,(69) and experienced users can implement their own custom algorithms.”
ImageJ2: ImageJ for the next generation of scientific image data (2017, methodology): “References for plugins shown: TrackMate [67], MaMuT [150], Multiview Reconstruction [70, 71], MotherMachine Analyzer (MoMA) [151, 152], Sholl Analysis [74], Kymograph Builder [153], Z-Spacing Correction [154], Trainable Weka Segmentation [155], Pendent Drop [156], SciView [157], BigDataViewer [66], ”
Xist nucleates local protein gradients to propagate silencing across the X chromosome (2021, methodology): “REAGENT or RESOURCE SOURCE IDENTIFIER Software and algorithms Fiji (Schindelin et al., 2012) https://fiji.sc/ ImageJ (Rueden et al., 2017) https://imagej.nih.gov/ij/ TrackMate (Tinevez et al., 2017) https://imagej.net/plugins/trackmate/ 3D ImageJ Suite (Ollion et al., 2013) https://imagej.net/plugin”
Deep learning for cellular image analysis (2019, methodology): “This approach has been implemented in software packages such as uTrack 119 , CellProfiler 24,25 , and TrackMate 120 , which can be applied to images of particles or cells.”