A collection for tracking microtubule dynamics, written in Python.

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Cell segmentation in phase contrast images


This Matlab code demonstrates an edge-based active contour model as an application of the Distance Regularized Level Set Evolution (DRLSE) formulation.


idTracker: Tracking animals


idTracker is a videotracking software that keeps the correct identity of each individual during the whole video. It works for many animal species including mice, insects (Drosophila, ants) and fish (zebrafish, medaka, stickleback). idTracker distinguishes animals even when humans cannot, such as for size-matched siblings, and reidentifies animals after they temporarily disappear from view or across different videos. It is robust, easy to use and general. Technique details and analyses of several applications are described in Pérez-Escudero et al (2014).

Video protocol:

Example image: Example video of 5 zebrafish

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Focal Adhesion Analysis Server


The website implements a set of computer vision algorithms designed to automatically process time-lapse images of fluorescently labeled focal adhesion proteins in motile cells. The methods associated with the processing have been published in PLOS One and Cell.

The publication describes a quantitative analysis of focal adhesion dynamics that have been imaged using TIRF. All image processing steps are well explained or referenced.

To better understand the dynamic regulation of focal adhesions, we have developed an analysis system for the automated detection, tracking, and data extraction of these structures in living cells. This analysis system was used to quantify the dynamics of fluorescently tagged Paxillin and FAK in NIH 3T3 fibroblasts followed via Total Internal Reflection Fluorescence Microscopy (TIRF). High content time series included the size, shape, intensity, and position of every adhesion present in a living cell. These properties were followed over time, revealing adhesion lifetime and turnover rates, and segregation of properties into distinct zones.

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Particle tracking with OMERO


u-Track is a client-side OMERO MATLAB application implementing the sophisticated multiple-particle tracking algorithm of Jaqaman et al. . It works on data previously imported into an OMERO server, and produces results in the form of MATLAB data structures as well as providing functionality to visualise these results.

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2D tracking using KNIME and Fiji


This simple KNIME workflow solution tracks 2D objects/cells in time series. After a few intensity based preprocessing steps, objects/cells are segmented first, then it uses Fiji TrackMate LAP method for the tracking task.

Documentation starts from p23 of the linked PDF. 

Example Image: mitocheck_small.tif (2.9M)

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Manual Tracking Components of ImageJ


The Fiji distribution of ImageJ comes with several manual tracking tools, two of which are particularly useful:

  • Plugins->Tracking->Manual Tracking
  • Plugins->Tracking->Manual tracking with TrackMate (TrackMate is an advanced automatic tracking tool, with the option for manual editing of tracks)

The Manual Tracking plugin is quick to use, intuitive and produces easy-to-understand output. TrackMate has the advantage that automatic detection and linkage can be combined with manual input.

Update sites

MtrackJ (**see the component page here) can be installed via Fiji update sites. It has many shortcut keys enabled so for manually tracking many data, it will become quite efficient as you get used to the short-cut key operation.


Pre-processing steps before manual tracking might include:

  • denoising and/or deconvolution
  • flicker and photobleaching correction, e.g. using Fiji's Image->Adjust->Bleach Correction
  • flat-field correction, and/or bandpass (ImageJ's Process->FFT->Bandpass filter) according to the size of the features of interest
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Automatic 2D/3D Tracking using ilastik


This workflow is used to track multiple (appear/disappear, dividing and merging) objects in presumably big 2D+t or 3D+t datasets. It is best suitable for roundish objects or spots. Tracking is done through segmentation, which can be obtained from ilastik pixel classification, or imported from other tools. Users should provide a few object level labels, and the software predicts results on the rest of the image or new images with similar image characteristics. As a result, all objects get assigned random IDs at the first frame of the image sequence and all descendants in the same track (also children objects such as daughter cells) inherit this ID.

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Tracking of focal adhesions in 2D time lapse movies


Tracking of focal adhesions includes a number of challenges:

  1. Detection of focal adhesion regions in areas of highly variable background
  2. Separation of "clumped" adhesions in different objects.
  3. Dynamics: Focal adhesions dynamically, grow, shrink, change their shape, they can fuse with neighboring adhesions or one adhesion can be split into multiple children.

The Würflinger et al (2011) describe how to detect focal adhesion objects and how to track them over time. Interestingly, tracking results are fed back to segmentation to improve separation of clumped adhesions.

The authors implemented the workflow in Matlab, but do not provide a ready-to-use script.

PDF link available here

Volocity - 3D Object Segmentation and Tracking


In the commercial image analysis software "Volocity", automated measurement protocols can be constructed by dragging, dropping and configuring a sequence of individual "tasks".

By combining the "Find Objects" task with a subsequent "Track" task, 3D objects can be identified and followed over time. The initial "Find Objects" segmentation can be refined, e.g. using "Separate Touching Objects"; and tracking results in the form of "Measurement Items" can be viewed in tabular form, as a graph, etc.