EMBL BioImage Data Analysis
EMBL BioImage Data Analysis
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Lecture notes and the manual for the practical is available in the following link:
We use Fiji in the practical work. Additionally, we use the following plugins.
If we might be able to also try
The following content is same as the PDF downloadable form the link above.
In the lecture, we will overview methods used for tracking in cell and developmental biology. Focus is made on the details of generally used two-step strategy namely particle detection and linking. In practical works, we use several image processing and analysis tools and explore tracking techniques for measuring EB3 movement in cultured cells. We try manual tracking, kymograph and automated tracking.
Time series of digital images, usually called ‘a stack’, contains temporal dynamics of position and intensity. By analyzing these dynamics, we can extract numerical parameter which then enables us to characterize the biological system. There are three types of dynamics.
Position does not change but intensity changes over time.
Position changes but the intensity does not change.
Both Position and Intensity change over time.
The lecture is about the measurement of type 2, generally called object tracking. Realistic situation demands type 3 e.g. you need to get a FRAP curve out of moving organelle, but we approximate the sample as type 2 as we need to learn the principle of tracking first, and type 3 will be your research that asks your ability to apply the full knowledge you acquired in the EMBL Advanced Imaging Course 2012, and probably some technique to use scripting languages.
For recent review on object tracking in cell and developmental biology, see Meijering et. al. (2012). This article also contains an extensive list of available tracking packages. A bit old review on single particle tracking by Saxton is still an excellent textbook affording rich biophysical insights for analyzing tracking results (Saxton and Jacobson 1997). For the classic “tracking” mentioned in the talk, or the presumptive fate map, see Vogt ()1925) only if you love biology (I am sure you do). This historical article could be downloaded via Google books. Magnificent movies produced by Keller et. al. using single plane illumination microscopy and extensive image processing that almost killed EMBL cluster by steaming it could be accessed through the web page of the paper in Science (Keller et al. 2008). Comparison of segmentation strategy in object tracking was published in 2001 by Cheezum et. al (2001) and has been a frequently cited paper in the field of biological object tracking. For overall review on image analysis methods see (Hamilton 2009).
Manual tracking ImageJ “manual tracker plugin”, we will do this in the practical.
Manual contour tracing and centroid
Local intensity maxima
Many applications for getting sub-pixel resolution coordinates.
You also saw this already in super-resolution microscopy talks by Ricardo and Melike.
Vaccinia Virus tracking example
Active Contour (SNAKES), Level-set
There is no tracking package that does tracking using machine learning based segmentation, but it is a strong candidate in coming days.
Fiji plugin “Advanced Weka Segmentation” http://fiji.sc/Advanced_Weka_Segmentation
Pattern matching (cross-correlation or sum-of-difference technique)
This is the simplest principle linking an object in one time point to the other.
Evaluation of Overlaps
Graph-theory based global optimization
Designing cost functions for the global optimization.
(I.F. Sbalzarini and Koumoutsakos 2005)
(Ivo F. Sbalzarini 2006)
“Tracklets”, a new way of dealing with dividing cells.
(Jaqaman and Danuser 2009)
(Bise, Yin, and Kanade 2011)
For single molecule trackers, there is a pretty new plugin called PTAj, written by Yoshiyuki Arai. We do not try this plugin in the practical but for those involved in single molecule imaging, it might be worth trying it at your home institute. The plugin offers sophisticated interface to review the tracking results. We all know that even though the tracking is automatic, we start checking each track manually just to be sure, and just to be sure…
There is also a new “all-in-one” tracking plugin for ImageJ. It’s name is TrackMate, written by Jean-Yves Tinevez. The linking algorithm includes the most-up-to-date “Tracklets” and allows to track merging/splitting objects. I have had not time to test this plugin thoroughly, but the interface is similar to Imaris and should be easy for people who are used to wizard-style parameter setting for tracking. For developers, the attractive feature of this plugin is that it offers interface to implement your own algorithm for segmentation and linking.
If capturing of EB3 image sequences were successful (I hope so!!), we use them. Otherwise, we use an example image sequence of EB1 labeled cells. EB1 behaves similar to EB1 and moves along microtubules. A file named eb18_b.tif is a sequence taken from single cultured cell labeled with eb1. This sequence is accessible by downloading an ImageJ plugin "Sample Image Loader". Instruction for installing this plugin is written in below.
Download and save the image files, the sequence you have taken or the example sequence eb18_b.tif in your local computer. We try several different ways to measure movement of EB3 or EB1 protein by tracking. Before tracking, examine the sequence first using stack related functions.
[File > Open...]
[Image > Properties...]
…Image stacks are by default taken as a z-series and not t-series. Set Slices to 1, and Frames to appropriate size (number of frames).
Explore stack functions. Start animation, Stop animation, change frame rates, Manipulations…These functions are located under
[Image > Stack >]
Running Z Projector plugin, written by Nico Stuurman, averages defined number of frames with moving window. This processing is powerful when you want to reduce noise from time-lapse sequences. Drawback is that some details of movement might be attenuated due to averaging. The plugin could be downloaded from
[Plugin > Install...]
Download the plugin "Sample Image Loader" linked in the following page and install just like you did with the running Z-projector.
In the simplest case, tracking can be done manually. The user can read out the coordinate position of the target object directly from the imaging software. In ImageJ, user can read out position coordinate indicated in the status bar by placing the cross-hair pointer over the object. Then coordinates can be listed in standard spreadsheet software such as Microsoft Excel or R for further analysis.
An ImageJ plug-in is also freely available to assist such simple way of tracking. An obvious disadvantage of the manual tracking is that the mouse-clicking by the user could be erroneous, as we are still human who gets tired after thousands of clicking. For such errors, measurement errors can be estimated by tracking the same object several times and this error could then be indicated together with the results. Otherwise, automated tracking is more objective, but if you have only limited number of tracks to analyze, manual tracking is a best choice before start to explore complex parameter space of automated tracking setting.
The manual tracking can be assisted by an ImageJ plug-in "Manual Tracking". This plug-in enables the user to record x-y coordinates of the position where the user clicked using mouse in each frame within a stack. The download site has a detailed instruction on how to use.
Manual Tracking (Manual_Tracking.class) is a plugin bundled with Fiji. If you are using ImageJ, download the plugin file from the link below. The plugin was written by Fabrice Cordelieres.
Your task now is to track the movement of individual EB3 signal using the ManualTracker PlugIn. To ease detection of the signal, enhance contrast and convert the image stack from 16-bit to 8-bit.
[Image > Adjust > Brightness and Contrast]
and then convert to 8-bit
[Image > Type > 8bit]
Check "centering correction", use Local Maximum.
Start manual tracking by clicking "Add track".
End tracking by "End Track"
Show tracks by "Drawing" Functions"
You could track different particles by repeating steps between 2 and 3. Results window will list the measured positions for particles and step 4 will show a track overlaid image stack.
Tracked data can be saved by activating "Result" window and
[File > Save as...]
Kymographs are a two-dimensional time traces, where time t is in Y axis and space along a one-dimensional contour is in X, and the dynamical variable *F(x,t) *is visualized as an image. Kymographs provide a fast and convenient way to visualize motion and dynamics in microscopy images.We try measuring the speed of EB1 or EB3 movement using Kymographs.
Open the image stack.
[Image > Adjust > Brightness and Contrast]
and then convert to 8-bit
[Image > Type > 8bit]
Do maximum intensity Z-projection
[Image > Stacks > Z-projection].
Choose segmented line ROI tool (to do this, right click line-ROI icon in the menu bar. You will see a drop down selection). With projection image generated above, trace one of the tracks in the projection image.
Go back to the stack (click the window and activate it - or bring it to the front - ),
[Edit > Selection > Restore Selection]
[Image > Stacks > Reslice...]
Don’t change parameters in the dialog window, simply OK. Resulting image is the kymograph.
To estimate the velocity of movement from kymograph, you simply need to measure the slope of the diagonal line shown in the kymograph. Since x-axis is distance and y-axis is time, if the starting point of the diagonal line is $(x_1, y_1)$ and the end point of the line is $(x_2, y_2)$ then the $slope = velocity$ is $$velocity = (x_2 - x_1) / (y_2 - y_1)$$ At the bottom of this section, you could find a simple macro to calculate this automatically from straight line selection.
ImageJ macro for measuring kymograph
To use this macro, do
[File > New > Script]
to launch script editor, then select
[Language > ImageJ Macro]
in the script editor menu, copy and paste the code above. You could also copy and paste from the following page.
OPTIONAL: Collect your data in Excel or R and plot tracks and a histogram of velocity distribution.
OPTIONAL: For those interested in using kymograph intensively, there is a good plugin that assists you in doing so efficiently. The plugin was written by Jens Rietdorf and Arne Seitz
OPTIONAL: To quantify ambiguous kymograph (meaning hard to draw a line, texture like streakes) use:
Written by myself, with valuable suggestions from Peter Lenert.
// scale and dt must be manually set var xyscale=0.207; //um var dt=0.69; //sec // sampling ROI coordinates, should be a straight line selection print("-------------------"); getLine(x1, y1, x2, y2, lineWidth); print("start (" +x1+" , "+y1+") end ("+ x2 + " , " + y2 + ") "); // calculation of speed. dx=abs(x2-x1); dy=abs(y2-y1); dx *= xyscale; dy *= dt; velocity= dx/dy; //output in log window. print("dx = "+ dx +"um during:" + dy + "sec"); print("Velocity = "+ d2s(velocity, 3) +" [um/s]");
This plugin uses linking algorithm that involves global optimization based on cost function that involves signal intensity and its second order derivative. The plugin was first written as a Matlab code by Ivo Sbalzarini and the migrated to ImageJ plugin by his group members, Guy Levy and Janick Cardinale.
The plugin could be down loaded from
To install the downloaded plugin,
[Plugins > Install...]. You
could also directly copy the file to plugins folder under Fiji
directory and do
[Help > Refresh Menus].
Just in case if the above plugin somehow does not work, try download and use the version linked in the flowing site.
Start the particleTracker by
[Plugins > Mosaic > ParticleTracker 2D/3D].
This tracking tool has two parts. First, all dots in each frame are detected, and then dots in successive frames are linked. The first task then is to determine three parameters for dot detection. There are three parameters.
Radius Expected diameter of dot to be detected in pixels.
CutOff Cutoff level for the none-particle discrimination criteria, a value for each dot that is based on intensity moment order 0 and 2.
Percentile Larger the value, more particles with dark intensity will be detected. Corresponds to the area proportion below intensity histogram in the upper part of the histogram.
Try setting different numbers for these parameters and click "Preview Detected". Red circles appear in the image stack. You could change the frames using the slider below the button.
After some trials, set parameters to what you think is optimum.
Two parameters for linking detected dots should be set.
Link Range …could be more than 1, if you want to link dots that disappears and reappears. If not, set the value to 1.
Displacement …expected maximum distance that dots could move from one frame to the next. Unit is in pixels.
After parameters are set, click "OK". Tracking starts.
When tracking is done, a new window titled "Results" appears. At the bottom of the window, there are many buttons. Click "Visualize all trajectories", and then a duplicate of the image stack overlaid with trajectories will appear.
Select a region within the stack using rectangular ROI tool and then click "Focus on Area". This will create another image stack, with only that region. Since this image is zoomed, you could carefully check if the tracking was successful or not.
If you think the tracking was not successful, then you should reset all the parameters and do the tracking again.
To analyze the results in R, data should be saved as a file. To do
so, first click "All Trajectories to Table". Results table will then
created. In this results table window, select
[File > Save As...]
and save the file on your desktop. By default, file type extension
is ".xls", excel format, but change this to ".csv". CSV stands for
"comma separated file", and this is more classic but general data
format which you could easily import in many software including R.
OPTIONAL: Import your results from Excel or R and plot tracks and a histogram of velocity distribution.
There is a PIV plugin bundled with Fiji, but we use another PIV plugin developed by Qingzong Tseng. You need several files for this plugin to run. Here is the recipe for running the plugin in 64-bit Windows OS.
and scroll the page down to “Installation” section. There you will find links to PIV_.jar, javacv.jar and jna.jar file. Download these three files.
Move or copy above three files to the plugin folder within Fiji or ImageJ folder.
(or you could click “template matching and slice alignment” link in the side bar in Zong’s page) and scroll down to “Installation” section. There you will find a link to OpenCV library for 64-bit windows. Download the file and unzip it. You will find two files, cv100.dll and cxcore100.dll, then.
Move or copy above two files, cv100.dll and cxcore100.dll, to the Fiji or ImageJ folder (not the plugin folder).
PIV plugin works only with two-frame stack. Reduce the stack size by Duplicating only part of the stack. To do so:
[Image > Duplicate...]then in the dialog panel, set the duplication range only two frames e.g. 10-11.
There are three different functions for doing PIV under
[Plugins > PIV >], Cross-Correlation, Basic and Advanced. Zong
recommends not to use Cross-Correlation. We try to do PIV using
When you choose
[Plugins > PIV > Basic], you will see a panel
asking you to input parameters. There are three iterations,
first start to estimate displacement of signal with large grids
and then to finer grids. By starting from larger grids,
estimation procedure recognizes global movement first and then
more detailed movements in later iterations. Try to set windows
size so that it fits the size of the image.
Final output is a text file. This could be saved anywhere, but be sure that you know where it is.
To plot the PIV field (Vectors),
[Plugins > PIV > Plot]. Try
to plot without changing the default value. If the plot looks
bad with too large / small arrows, close the plot and do the
plotting again with sums adjusted parameters.
OPTIONAL Since output is a text file of vector values, you could import the text file from Excel or R to plot histogram of velocities.
Bise, Ryoma, Zhaozheng Yin, and Takeo Kanade. 2011. “Reliable cell tracking by global data association.” In 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 1004–1010. IEEE. http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5872571.
Cheezum, M. K., W. F. Walker, and W. H. Guilford. 2001. “Quantitative comparison of algorithms for tracking single fluorescent particles.” Biophysical journal 81 (oct): 2378–88. doi:10.1016/S0006-3495(01)75884-5. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1301708\&tool=pmcentrez\&rendertype=abstract.
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