MTracker NewLogo-04.png
Microtubule Tracker
Project Fiji
URL http://imagej.net/MicrotubuleTracker
Source on GitHub
License GPLv3
Date Wed Jul 19 12:00:45 CDT 2017
Development status Active
Support status Active
Founders SimoneR, Stephan Preibisch, Varun Kapoor
Leads Varun Kapoor
Developers Varun Kapoor, Stephan Preibisch
Debuggers Varun Kapoor, Stephan Preibisch, SimoneR, William Hirst, Christoph Hentschel
Reviewers Varun Kapoor
Support Varun Kapoor
Maintainers Varun Kapoor
Contributors Varun Kapoor, Stephan Preibisch, SimoneR, William Hirst, Christoph Hentschel

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 microtubule seeds 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) and is able to compute population statistics such as length distributions.

To make yourself familiar with MTrack, please go to the Example section section where you are able to download an example TIRF movie and you will find detailed instruction for running it.


  1. Click Help ▶ Update....
  2. Click the Manage update sites button.
  3. Select the MTrack update site in the list.
  4. Click Close and then click Apply changes.
  5. Restart Fiji.
  6. Launch the plugin with Plugins ▶ MTrack.


Module 1 - Microtubule Detection & Tracking

A typical dataset consists of a single two-dimensional (2d) image of the non-dynamic microtubule seeds followed by a 2d time-lapse of the dynamically growing microtubules. The file format can be any format readable by Fiji/Bioformats (.tif, .nd2, … ). To run the tracker select Plugins ▶ MTrack ▶ Tracking

The welcome panel will open.


Choose Mode

For a first analysis of your data, we suggest using the simple mode, in which we have pre-selected a number of parameters. In case you are unsatisfied with the outcome of the tracking you can use the advanced mode(MTrack) to fine-tune settings. When analyzing more than one movie, you can select batch mode(MTrack) and run many movies simultaneously. However, before running the program in batch mode, you have to at least run the program once in single or advanced mode to select and save the required parameters. The following intro is on simple mode.

Select Movie

Next, the user selects the movie. The movie to be uploaded is the original movie coming out of the microscope. Object recognition of Microtubules requires a preprocessed movie and such a preprocessed movie is only used for locating where the microtubules are in the image and not for end-points detection, which is always performed on the original image. In simple mode the program does a pseudo flat field correction by default and the user only has to input the microscope parameters and go next to start the tracking module.

In the advanced mode the user has the option to either perform a flat-field correction and apply a median filter of chosen radius or user can also upload their own preprocessed movie. (Read more about Preprocessing(MTrack)). For the movie type choose one of the three supported options:

  • Two channel image as hyper stack (both channels in one image)
  • Concatenated seed image followed by time-lapse images
  • Single channel time-lapse images

Please choose an output file name and directory. The trajectory files will be written as .txt files. By default trajectories will be saved in the current working directory with the name of the movie.

Microscope Parameters

The program automatically reads the metadata shown as pixel size (micrometer in x and y) and frame rate (in seconds). If the metadata could not be read properly, the user can change the values. In addition, the user is asked to enter the Sigma(X) and Sigma(Y) of the Point-Spread-Function (PSF) of the microscope in pixel units see here for more explanation. For your convenience, our software comes with an inbuilt PSF analyzer tool, which can optionally determine the PSF of your microscope from bead images by fitting a Gaussian function. When you input any parameters, please ensure that you use decimal number formatting only.


As mentioned before, for recognizing microtubules in the image, preprocessing is essential, in advanced mode we provide the user with an option to do pseudo-flat field correction and to apply a median filter of certain radius (e.g. r=2 pixels). Optionally user can load their own preprocessed movie. Please note that this blurring will not be used during subsequent tracking. In simple mode only pseudo-flat field correction is done.

Press Next to proceed, and 3 screens and one panel will open. They show the original movie, the preprocessed movie, and the “active image”, which representing the seeds and is typically the first frame of the movie. Every successfully recognized seed will be marked with a red ellipse.

MSER parameters

The default algorithm to identify the seeds as objects is called Maximally Stable Extremal Regions (MSER) [1].Read more about MSER parameters. If seeds are not recognized or two very close seeds are recognized as one, the user can change the MSER parameters using the adjustable sliders. The effect will be displayed live on the “active image”. Once most seeds are correctly recognized as objects, click “Find endpoints” to detect the ends of each seed with sub-pixel accuracy.

The end-points will be displayed as green circles and a “Next” button appears on the panel which allows the user to flip to the next panel.



Before starting the actual tracking of the dynamically growing microtubules, the program will give you several options:


Deselect and select ends

If an end was wrongly recognized or two points are too close, you can deselect an end by left clicking on it in the image. The program will remember and allow to re-select this end by clicking Shift + left click (pink circle will mark the end). In case an end has not been recognized, use Shift + Alt + left click to select a user defined end (orange circle will mark the end). Read more on microtubule polarity and (+) end vs. (-) end tracking.

You can select the start and end time over which the tracking will be performed by entering the frame numbers. Yellow ellipses mark seeds to be tracked, red ellipses mark seeds which won’t be tracked. Green circles mark ends to be tracked. Orange circles mark user defined ends and will be tracked. Click “Confirm ends and track” to perform the actual tracking, which will be performed “live” (progress bar will show). A yellow crosshair will show the current position of the tracking on each marked microtubule end. A "Success" frame will let you know about the end of the tracking. Two movies will be displayed, the “Track ID” movie, which can be used to link the trajectories to individual microtubules and an “Overlay movie”, in which the user can recapitulate the tracking. The trajectory of each end is individually saved as .txt file and numbered according to the track ID. After successful tracking, the user has the option to save the selected ends, so that the movie can be run (again) in batch mode.

Module 2- Microtubule dynamics

Microtubules show a dynamic behavior known as dynamic instability, which is characterized by four parameters (1) polymerization velocity (vp, nm/sec), (2) depolymerization velocity (vd, nm/sec), (3) catastrophe frequency (fcat, sec-1), and (4) rescue frequency (fres, sec-1). Using the trajectories created in Module 1 (The .txt file contains frame number, total length (in px and μm), track ID, x and y position (pixel and μm) and the length increment from the previous frame (px and micrometer). Module 2 derives these dynamic parameters by fitting models using RANSAC. (Read more on MTrack-RANSAC models).

If not forwarded by Module 1, Module 2 can be selected by Plugins ▶ MTrack ▶ Ranssac Rate Analyzer

The panel that opens will allow the user to select individual files containing trajectories which were generated in the first module, which will be displayed as length versus time plots on which RANSAC fits a model of microtubule dynamics using the default parameters. Read more about the MTrack-RANSAC parameters. If the default parameters work well the rates and averages are displayed in front of the file in the table. If the program notices that two succeeding growth events follow without any catastrophe event that row will be marked red indicating to the user that they need to change the parameters for this file for getting proper Ransac fits.

After the user has gone through certain files in the table shown in the panel below they can click "compute rates and frequencies" in which case average growth/shrink rates and a count of each growth and shrink event along with catastrophe and rescue frequency for each trajectory is computed and stored in the current directory. Program creates two files labelled "Allrates.txt" and "Allaverages.txt", in the first file it contains the information about the start and end time and rates of each growth and shrink events along with the trajectory filename for easy identification of to which movie does the computation belongs to. In the second file it contains the averaged rate and frequency information of each trajectory analyzed up to then.

In addition, the user can obtain microtubule length distribution for a certain time point or a time-averaged distribution. In the length distribution plot, the mean length, and the standard deviation will be displayed after fitting an exponential decay curve to the obtained distribution.

Ransacfirst.png Click here to see some examples of the MTrack-Ransac fits.


An example movie with several dynamic microtubules is available for download here. To perform the analysis of this movie:

  1. Put the demo movie MTrack_Demo.tif into an empty directory, the results will also be stored here.
  2. To run the MicroTubule Tracker.
  3. Select Plugins, Microtubule Detection and Tracking.
  4. Select Simple Mode, Concatenated Seed Image followed by time-lapse images, choose the file, the microscope parameters will be automatically loaded, finally click Next> to continue.
  5. Using the default MSER parameters 7 microtubule seeds will be identified, click Find Endpoints to continue.
  6. The correct endpoints of 6 microtubule seeds will be identified (one is too short and could be added manually), click Next> to continue.
  7. Click Confirm the end(s) and track to track the microtubules over all 241 time-points. The expected runtime is around 6-7 min.
    1. Note: the few warnings of missed assignments can be safely ignored, these timepoints will simply be missing, which does not create any further problems as long as it is not happening in the majority of cases.
  8. Each microtubule trajectory will be saved
  9. You are now able to review the tracking results in the ImageJ windows, click Enter RANSAC stage> to continue.
  10. Click Select directory of MTrack generated files and select the directory that now contains all the text files with the tracking results
  11. To adjust the parameters to automatically derive microtubule dynamics select one of the microtubule seed end points, for example MTrack_DemoSeedLabel1Plus.txt, which will be displayed in the Microtubule Length Plot window
  12. Since shorter growth events are not identified using the standard parameters, change the Minimum number of timepoints to 20 using the slider or by typing it and pressing return
  13. Optionally, change the Linear Function (only) to Linearized Quadratic Function to achieve a fit with a lower error


Please note that MTrack is available through Fiji, and is based on a publication. If you use it successfully for your research please be so kind to cite our work:

Varun Kapoor, William G. Hirst, Christoph Hentschel, Stephan Preibisch and Simone Reber, “MTrack: Automated Detection and Tracking of Dynamic Microtubules” [2]


  1. Robust wide-baseline stereo from maximally stable extremal regions, J Matas, O Chum, M Urban, T Pajdla Image and vision computing 22 (10), 761-767.
  2. https://www.biorxiv.org/content/early/2018/07/13/368191