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TrackMate Accuracy

2,055 bytes added, 03:49, 25 January 2015
Comment accuracy results.
Finally, for SNR<4, I filtered out tracks that had less than 4 detections.
 
 
 
 
 
=== Results. ===
 
The results for each scenario is presented and commented below. Since we used the same detector for all scenarios, all the measures have s common shape for their dependence on SNR.
 
Basically, accuracy is the same for SNR ≥ 4. Below SNR = 3 included, the detector is unable to reliably finds all the particles. For a SNR of 2 and 3, it still finds a subset of correct particles, amongst the brightest. At a SNR of 1, detection results are dominated by spurious detections, and the particle linking algorithm performance does not matter anymore. All are equally bad, since they track the wrong particles.
The RMSE on particle position is the worst here over the 4 scenarios. This is the obvious consequence of the particle shape, which is asymmetric and elongated, when the LoG detector expects bright blobs. Still, this does not affect tracking results.
 
[[File:TrackMate MICROTUBULE LAP Brownian motion, Linear motion tracker &amp; Nearest neighbor.png|600px]]
 
 
 
[[File:TrackMate MICROTUBULE LAP Brownian motion, Linear motion tracker &amp; Nearest neighbor.png|600px]]
==== Receptor scenario. ====
 
TrackMate does not have a particle linking algorithm that specifically address this scenario. The motion model switches from Brownian motion to linear motion, and we have algorithm that deal with one or the other. It is no surprise therefore to find that they all perform similarly.
 
Fortunately, accuracy values are rather good and do not break down too much against particle number. We also see that the linear motion tracker behaves slightly better than the rest in all conditions.
[[File:TrackMate RECEPTOR LAP Brownian motion, Linear motion tracker &amp; Nearest neighbor.png|600px]]
 
 
 
==== Vesicle scenario. ====
 
The motion model of this scenario is the pure Brownian motion. Unsurprisingly the LAP tracker behaves the best as it models precisely this situation. The linear motion tracker is confused by the constant direction changed generated by the random motion, and is superseded even by the nearst neighbor search.
[[File:TrackMate VESICLE LAP Brownian motion, Linear motion tracker &amp; Nearest neighbor.png|600px]]
 
 
 
==== Virus scenario. ====
 
As for the receptor scenario, TrackMate does not have a specific tracker for this scenario. The main difference with the receptor scenario is that the linear motion part of the trajectories are all following the same direction. Again, TrackMate cannot exploit this bit of information, but it is enough to change what is the better performing algorithm (compared to the receptor scenario).
 
Also, this scenario was the only one to ship 3D data over time. Thanks to ImgLib2 dimensional genericity, TrackMate does not mind.
[[File:TrackMate VIRUS LAP Brownian motion, Linear motion tracker &amp; Nearest neighbor.png|600px]]
 
 
 
== References. ==
<references/>
 
 
 
[[User:JeanYvesTinevez|JeanYvesTinevez]] ([[User talk:JeanYvesTinevez|talk]]) 03:49, 25 January 2015 (CST)
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