"PTA2 is an ImageJ1.x plugins that enable automatic particle tracking"

This plugin is developed specifically for single-molecule imaging, so it's good at tracking spots with noisy background. 

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SIMPLETRACKER a simple particle tracking algorithm that can deal with gaps.

Tracking , or particle linking, consist in re-building the trajectories of one or several particles as they move along time. Their position is reported at each frame, but their identity is yet unknown: we do not know what particle in one frame corresponding to a particle in the previous frame. Tracking algorithms aim at providing a solution for this problem. 

simpletracker.m is - as the name says - a simple implementation of a tracking algorithm, that can deal with gaps. A gap happens when one particle that was detected in one frame is not detected in the subsequent one. If not dealt with, this generates a track break, or a gap, in the frame where the particle disappear, and a false new track in the frame where it re-appear. 

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2D and 3D tracking based on global cost function optimization


The workflow consists of firstly identifying spot (which can be also gravity center of cells identified by another method), and then secondly compute trajectories by linking these spots by global optimisation with a cost function. This method is part of the methods evaluated in Chanouard et al (2014) as "method 9" and is described in detail in its supplementary PDF (page 65).


Following plugins are required.

  1. JAR to be placed under IJ plugin directory
    1. A pdf file with instructions and output description is also available in the zip .
  2. MTrackJ : Used for visualization of tracks. Preinstalled in Fiji.
  3. Imagescience.jar: This library is used by MTrackJ. Use update site to install this plugin.
  4. jama.jar. Preinstalled in Fiji.


  • support blinking (with a parameters allowing it or not)
  • fast,
  • can be used in batch, some analysis results provided.
  • No dynamic model.
  • The tracking part is not dependent of ImageJ.


  • does not support division
  • the optimization algorithm used is a simulated annealing, so results can be slightly different between two runs.
  • No Dynamic model (so less good results but can be used for a first study of the kind of movements)

The sample data

The parameters used for this example data Beads, were

  1. detection: 150
  2. the max distance in pixels: 20
  3. max allowed disappearance in frame: 1

Quantitative analysis of focal adhesion dynamics


The quantification is explained in detail in chapter 8 "Cell Polarity - Focal Adhesion and Actin Dynamics in Migrating Cells" in "Bioimage Data Analysis Book" downloadable from here.

For codes and sample images, download the zipped archive (linked under "Download").

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u-track is a multiple-particle tracking Matlab software that is designed to (1) track dense particle fields, (2) close gaps in particle trajectories resulting from detection failure, and (3) capture particle merging and splitting events resulting from occlusion or genuine aggregation and dissociation events. Its core is based on formulating correspondence problems as linear assignment problems and searching for a globally optimal solution.

Data can be read using bio-format and interfaced with OMero data base.

It comes as a standalone software, but can be used as a library, which is according to the authors the most widely used version of it.

  • Version 2.2 adds parallel processing functionality for multi-movie datasets when using the GUI.
  • Version 2.1 enables the analysis of movies stored on an OMERO server
  • Version 2.0 includes two new tracking applications: microtubule plus-end tracking (previously distributed as plusTipTracker) and nuclei tracking
  • A third optional processing step has been added to the analysis workflow, track analysis, with two methods: motion analysis and microtubule plus-end classification

For more information, please see Jaqaman et al., Nature Methods 5, pp. 695-702 (2008). Besides basic particle tracking, the software supports the features described in Applegate et al. J. Struct. Biol. 176(2):168-84. 2011 for tracking microtubule plus end markers; and in Ng et al. J. Cell Biol. 199(3):545-63. 2012 for tracking fluorescently-labeled cell nuclei.




TrackMate provides the tools to perform single particle tracking (SPT). SPT is an image analysis challenge where the goal is to segment and follow over time some labeled, spot-like structures. Each spot is segmented in multiple frames and its trajectory is reconstructed by assigning it an identity over these frames, in the shape of a track. These tracks can then be either visualized or yield further analysis results such as velocity, total displacement, diffusion characteristics, division events, etc...