This first panel allows you to check the spatial and temporal calibration of your data. It is very important to get it right, since everything afterwards will be based on physical units and not in pixel units (for instance μm and minutes, and not pixels and frames). In our case, that does not matter actually, since our test image has a dummy calibration (1 pixel = 1 pixel).
What is critical is also to check the dimensionality of the image. In our case, we have a 2D time-lapse of 50 frames. If metadata are not present or cannot be read, ImageJ tends to assume that stack always are Z-stack on a single time-point.
The LoG-based segmenters fortunately demand very few parameters to tune them. The only really important one is the ''Estimated blob diameter'''. Just enter the approximate size of the spots you are looking to tracks. Careful: you are expected to enter it in <u>physical units</u>. In our dummy example, there is no calibration (1 pixel = 1 pixel), so it does not appear here.
There are extra fields that you can configure also. The '''Threshold''' numerical value aims at helping dealing with situation where a gigantic number of spots can be found. Every spot with a maximal intensity value below this threshold value will not be retained, which can help saving memory. You set this field manually, or by adjusting the threshold using ImageJ: call the ''Image > Adjust > Threshold'' menu item (Ctrl + Shit + T), adjust the upper threshold to your liking, the press the '''Refresh''' button on the panel. This will grab the value you just set.
You can check '''Use median filter''': this will apply a 3x3 median filter prior to any processing. This can help dealing with images that have a marked salt & pepper noise which generates spurious spots.
We hope that TrackMate will be used in experiments requiring '''Sub-pixel localization''', such as following motor proteins in biophysical experiments, so we added schemes to achieve this. The one currently implemented uses a quadratic fitting scheme (made by Stephan Saalfeld and Stephan Preibisch) based on [http://www.cs.ubc.ca/~lowe/keypoints/ David Lowe SIFT work]<ref>David G. Lowe, "Distinctive image features from scale-invariant keypoints,
" International Journal of Computer Vision, 60, 2 (2004), pp. 91-110.</ref>. It has the advantage of being very quick, compared to the segmentation time itself.
The two others automated segmenters share more or less the same fields in their own configuration panel. The '''Downsampled LoG segmenter''' simply asks for an extra down-sampling integer factor.
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.
You can stack several filters by simply clicking on the green '''+''' button. TrackMate will retain the spots that satisfy to <u>all</u> (logical ''and'') the criteria set by the filters.
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== References ==