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Getting started with TrackMate

5,464 bytes added, 16:04, 15 January 2012
This is why there is spot features and feature filters. In the next steps, each spot will have a series of numerical features calculated using its location, radius and the image it is found in, such as the mean pixel intensity. You will be able to define filters on these features, to retain only the ones that are relevant to your study.
But for a very large number of spots - let's say: more than 1 million of them - performance issues can kick in. Those millions of spots will be stored in the model, and saved in the TrackMate file, in case you want to step back and change the filters , because for instance you realized you are not happy with the end results (you can do that). Some visualization tools - the 3D displayer for instance - will generate the renderings for those millions of spots at once and hide or show them depending on the filter values, because it is too expensive to recreate the renderings while tuning the filter values.
To deal with that, we added a first filter prior to any other step, that uses the '''Quality''' value. The quality value is set by the segmenter, and is an arbitrary measure of the likelihood of each spot to be relevant. This panel collects all the quality values for all spots, and display their histogram (Y-scale is logarithmic). You can manually set a threshold on this histogram by clicking and dragging in its window. All spots with a quality value below this threshold will be '''discarded'''. That is: they will be deleted from the process, not saved in the file, they won't be displayed, nor their features will be calculated. Which is what we want when meeting a gigantic number of spurious spots. Note that this step is '''irreversible''': if you step back to this panel, you will see the original histogram, but beware that the spots you have discarded cannot be recovered by changing the threshold. The only way is to step back further and restart the segmentation step.
In our case, we see from the histogram that we could make sense of this step. There is a big peak at low quality (around a value of 1.2) that contains the majority of spots and is most likely represent spurious spots. So we could put the threshold around let's say 5.5 and probably ending in having only relevant spots. But with less than 10 000 spots, we are very very far from 1 million so we need not to use this trick. Leave the threshold bar close to 0 and proceed to the next step.
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== Selecting a displayer ==
[[Image:TrackMate DisplayerChoice.png|right|border|]]
Here, you can choose between the two visualization tools that will be used to display the tracking results. The first one, '''HyerpStack displayer''', simply reuses ImageJ stack window and overlay the results non-destructively over the image. Choosing the '''3D viewer''' will open a new 3D viewer window, import that image data in it, and will display spots as 3D spheres and tracks as 3D lines.
Honestly, choose the HyperStack displayer. Unless you have a very specific and complicated case that needs to inspect results in 3D immediately, you do not need the 3D viewer. The HyperStack displayer is simpler, lighter, allow to manually edit spots, and you will be able to launch a 3D viewer at the end of the process and still get the benefits.
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== Computing spot feature and rendering displayer ==
[[Image:TrackMate ComputeSpotFeatures.png|right|border|]] We are now back to the log panel. As you pressed the '''Next''' button, two process started: * the features of all spots (well, those you left after the initial filtering step) are calculated;* the displayer selected does everything it needs to prepare displaying them. So nothing much. When the '''Next''' button is re-enabled, press it to move to the spot filtering step.  <br style="clear: both" /> == Spot and spot filtering ==
[[Image:TrackMate FilterSpots 1.png|right|border|]]
The moment this panel is shown, the spots should be displayed on the ImageJ stack. They take the shape of purple circles of diameter set previously. As promised, there are quite a lot of them, and their vast majority are irrelevant. If you did not remove the irrelevant one in the initial thresholding step, you should get an overlay that resembles the image to the right.  Trying to do particle linking on all these spots would be catastrophic, and there would be no hope to make sense of the data as it is now. This is why there is this spot filtering step, where you can use the features we just calculated to select the relevant spots only.  The spot filtering panel is divided in two. The upper part, which is empty now, contains the filter you define, in the shape of histograms. We will come back to them soon. The bottom part contains the '''+''' and '''minus''' buttons that allow to respectively add or remove a feature filter, and a combo-box to set the display color of the spots. Let us try it to play with it to find the best feature to filter out spurious spots.  By default , when the combo-box is on '''Default''', all spots are purple. By clicking on it, you see that you can select amongst all the possible features calculated. For instance, if you select '''X''', the spots will be colored according to their X position. A colored bar below the combo-box indicates the range the color gradient corresponds to.  '''X''' does not seem to be a good feature to select relevant spots. We know that '''Quality''' should be, by construction, but let us pick '''Mean intensity'''. By scrolling through the time slide you should be able to see that now all the spurious spots have a blue to turquoise color, whether the real one stands forward in red or yellow.  We will therefore add a filter based on this feature. Click the green '''+''' button. A small orange box should appear in the upper part, containing the histogram for a given feature. Click on the orange box combo-box to select '''Mean intensity'''. Yous should have something similar to the image below. [[Image:TrackMate FilterSpots 2.png|right|border|]] We note that the histogram has a very desirable shape: a massive peak at low intensity represent most of the spots. There are other smaller peaks at higher intensity, and fortunately, they are very well separated from the large peak.  To move the threshold, simply click and drag inside the histogram window. Notice how the overlay is updated to display only the remaining spots after filtering. A filter can be set to be above or below the given threshold. You change this behavior using the radio buttons below the histogram window. In our case, we want it to be above of course. The '''Auto''' button uses [ Otsu's method] to determine automatically a threshold. In our case, we will put it manually around 33.
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