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Getting started with TrackMate

387 bytes removed, 03:53, 1 August 2013
Selecting a simple tracker
The apparent profusion of choices should not disorient you, for it just that: an appearance. We chose to focus on the Linear Assignment Problem (LAP) in the framework first developed by Jaqaman ''et al.''<ref name="Jaqaman">[http://www.nature.com/nmeth/journal/v5/n8/full/nmeth.1237.html Jaqaman et al., "Robust single-particle tracking in live-cell time-lapse sequences", Nat Methods. 2008 Aug;5(8):695-702.]</ref>.
All the The first 4 two LAP trackers are based on LAP, with important differences from the original paper described elsewhere[[TrackMate_algorithms#Main_differences_with_the_Jaqaman_paper.5B1.5D|here]] . We focused on this method for it gave us a lot of flexibility and it can be configured easily to handle most cases. You can tune it to allow ''splitting events'', where a track splits in two, for instance following a cell that encounters mitosis. ''Merging events'' are handled too in the same way, though my small culture prevents me from quoting a relevant biological case obvious as the previous one. More importantly are ''gap-closing'' events, where a spot disappear for one frame (because it moves out of focus, because segmentation fails, ...) but the track manages to recuperates and connect with re-appearing reappearing spots later.
Here are the main differences between the 4 '''These LAP trackers'''. The LAP framework needs internally an solver that can deal with the [http://en.wikipedia.org/wiki/Assignment_problem assignment problem]. We implemented algorithm exists in TrackMate in two of these algorithmsflavors: the [http://en.wikipedia.org/wiki/Hungarian_algorithm Hungarian algorithm] which is the classical a simple one, and a new not simple one, based on ideas by [http://en.wikipedia.org/wiki/Edmonds%E2%80%93Karp_algorithm Edmond, Karp and Tomizawa] that solves There are again the same problem , but much faster. They give the same results, so you can always use the fast simple onespropose fewer configuration options and a thus more concise configuration panel.In short:
Each of these algorithms exists in two flavors: a * The simple one only allows to deal with gap-closing events, and prevent splitting and a not simple onemerging events to be detected. There are again the sameAlso, but the simple ones propose fewer configuration options and a thus more concise configuration panelcosts to link two spots are computed solely based on their respective distance. In short:
* The not simple ones only allow one allows to deal with detect any kind of event, so if you need to build tracks that are splitting or merging, you must go for this one. If you want to forbid the detection of gap-closing events, and prevent splitting and merging events you want to be detecteduse it as well. Also, you can alter the costs cost calculation to link two disfavor the linking of spots are computed solely based one their respective distancethat have very different feature values.
* The not There is also a 3rd tracker, the [http://en.wikipedia.org/wiki/Nearest_neighbor_search '''Nearest neighbor search'''] tracker. This is the most simple ones allow to detect any kind of event, so if tracker you need to can build tracks that are splitting or merging, you must go and it is mostly here for these onesdemonstration purposes. If you want Each spot in one frame is linked to forbid another one in the detection of gap-closing eventsnext frame, disregarding any other spot, you want to use it as wellthus achieving only a very local optimum. Also, you You can alter set a maximal linking distance to prevent the cost calculation results to disfavor the linking be totally pathological, but this is as far as it goes. It may be of spots that have use for very different feature valueslarge and easy datasets: its memory consumption is very limited (at maximum only two frames need to be in memory) and is quick (the nearest neighbor search is performed using [http://en.wikipedia.org/wiki/Kd_tree Kd-trees]).
There Then of course, there is also a 5th tracker, the [http://en.wikipedia.org/wiki/Nearest_neighbor_search option to skip the automated tracking using '''Nearest neighbor searchManual tracking'''] tracker. This is the most simple tracker you can build, and it is mostly here for demonstration purposes. Each spot in one frame is linked to another one in the next frame, disregarding any other spot, thus achieving only a very local optimum. You can set a maximal linking distance to prevent the results to be totally pathological, but this is as far as it goes. It may be of use for very large and easy datasets: its memory consumption is very limited (at maximum only two frames need to be in memory) and is quick (the nearest neighbor search is performed using [http://en.wikipedia.org/wiki/Kd_tree Kd-trees]).
Right now, in our first trial, let us pick the '''Simple fast LAP tracker'''.
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