Object tracking

The workflow consists of firstly identifying spot (which can be also gravity center of cells identified by another method), and then secondly compute trajectories by linking these spots by global optimisation with a cost function. This method is part of the methods evaluated in Chanouard et al (2014) as "method 9" and is described in detail in its supplementary PDF (page 65).

Dependencies

Following plugins are required.

1. JAR to be placed under IJ plugin directory
2. A pdf file with instructions and output description is also available in the zip .
3. MTrackJ : Used for visualization of tracks. Preinstalled in Fiji.
4. Imagescience.jar: This library is used by MTrackJ. Use update site to install this plugin.
5. jama.jar. Preinstalled in Fiji.

##Advantages:

• support blinking (with a parameters allowing it or not)
• fast,
• can be used in batch, some analysis results provided.
• No dynamic model.
• The tracking part is not dependent of ImageJ.

Pitfalls:

• does not support division
• the optimization algorithm used is a simulated annealing, so results can be slightly different between two runs.
• No Dynamic model (so less good results but can be used for a first study of the kind of movements)

##The sample data

The parameters used for this example data Beads, were

1. detection: 150
2. the max distance in pixels: 20
3. max allowed disappearance in frame: 1

A collection for tracking microtubule dynamics, written in Python.

This Matlab code demonstrates an edge-based active contour model as an application of the Distance Regularized Level Set Evolution (DRLSE) formulation.

idTracker is a videotracking software that keeps the correct identity of each individual during the whole video. It works for many animal species including mice, insects (Drosophila, ants) and fish (zebrafish, medaka, stickleback). idTracker distinguishes animals even when humans cannot, such as for size-matched siblings, and reidentifies animals after they temporarily disappear from view or across different videos. It is robust, easy to use and general. Technique details and analyses of several applications are described in Pérez-Escudero et al (2014).

Video protocol: https://www.youtube.com/watch?v=oC9tp5TKAyw

Example image: Example video of 5 zebrafish

The website implements a set of computer vision algorithms designed to automatically process time-lapse images of fluorescently labeled focal adhesion proteins in motile cells.

The methods associated with the processing have been published in PLOS One and Cell. The publication describes a quantitative analysis of focal adhesion dynamics that have been imaged using TIRF. All image processing steps are well explained or referenced.

To better understand the dynamic regulation of focal adhesions, we have developed an analysis system for the automated detection, tracking, and data extraction of these structures in living cells. This analysis system was used to quantify the dynamics of fluorescently tagged Paxillin and FAK in NIH 3T3 fibroblasts followed via Total Internal Reflection Fluorescence Microscopy (TIRF). High content time series included the size, shape, intensity, and position of every adhesion present in a living cell. These properties were followed over time, revealing adhesion lifetime and turnover rates, and segregation of properties into distinct zones.