Manual tracking with TrackMate


Manual tracking using Trackmate plugin (comes with FIji, so no installation required if you are using Fiji). 

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HyphaTracker Workflow

HyphaTracker propose a workflow for time-resolved analysis of conidia germination. Each part of this workflow can also be used independnatly , as a toolbox. It has been tested on bright-field microscopic images of conidial germination. Its purpose is mainly to identify the germlings and to remove crossing hyphae, and measure the dynamics of their growth.




MaMuT is an end user plugin that combines the BigDataViewer and TrackMate to provide an application that allow browsing, annotating and curating annotations for large image data.

kymograph generation


Kymograph generation under ImageJ:

one simple solution, plot a line (ROI line) on the first frame, where you want to generate the kymograph.


Image  / Stacks  / Reslice

It will generate a new image were Y dimension is the time, and X the position on the line you have drawn.

need a thumbnail



  FlyLimbTracker is  a method that uses active contours to semi-automatically track body and leg segments from video image sequences of unmarked, freely behaving Drosophila flies. This approach can be used to measure leg segment motions during a variety of locomotor and grooming behaviors.

For now the plugin have to be downlaoded directly from the EPFL website (see link), not from the search bar as usual in ICY.


Drosophila track legs

MicrotubuleTracker in FIJI


MTrack is a tool, which detects, tracks, and measures the behavior of fluorescently labeled microtubules imaged by TIRF (total internal reflection fluorescence) microscopy. In such an in vitro reconstitution approach, stabilized, non-dynamic microtubule seeds serve as nucleation points for dynamically growing microtubules.

MTrack is a bi-modular tool. The first module detects and tracks the growing microtubule ends and creates trajectories. The second module uses these trajectories to fit models of dynamic behavior (polymerization and depolymerization velocities, catastrophe and rescue frequencies). It also computes statistics such as length and lifetime distributions when analyzing more than one movie (batch mode).

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Track Filament shaped objects and analyze tracks using Ransac fits.




QuimP is software for tracking cellular shape changes and dynamic distributions of fluorescent reporters at the cell membrane. QuimP's unique selling point is the possibility to aggregate data from many cells in form of spatio-temporal maps of dynamic events, independently of cell size and shape. QuimP has been successfully applied to address a wide range of problems related to cell movement in many different cell types. 


In transmembrane signalling the cell membrane plays a fundamental role in localising intracellular signalling components to specific sites of action, for example to reorganise the actomyosin cortex during cell polarisation and locomotion. The localisation of different components can be directly or indirectly visualised using fluorescence microscopy, for high-throughput screening commonly in 2D. A quantitative understanding demands segmentation and tracking of whole cells and fluorescence signals associated with the moving cell boundary, for example those associated with actin polymerisation at the cell front of locomoting cells. As regards segmentation, a wide range of methods can be used (threshold based, region growing, active contours or level sets) to obtain closed cell contours, which then are used to sample fluorescence adjacent to the cell edge in a straightforward manner. The most critical step however is cell edge tracking, which links points on contours at time t to corresponding points at t+1. Optical flow methods have been employed, but usually fail to meet the requirement that total fluorescence must not change. QuimP uses a method (ECMM, electrostatic contour migration method (Tyson et al., 2010) which has been shown to outperform traditional level set methods. ECMM minimises the sum of path lengths connecting all pairs of points, equivalent to minimising the energy required for cell deformation. The original segmentation based on an active contour method and outline tracking algorithms have been described in (Dormann et al., 2002; Tyson et al., 2010; Tyson et al., 2014).




Manual Tracking GUI. Many shortcut keys, and after being experienced, manual tracking can efficiently done. Post-editing capability to delete segments, merge and splitting tracks is quite useful.

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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



A collection for tracking microtubule dynamics, written in Python.

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