Object tracking

u-Track is a client-side OMERO MATLAB application implementing the sophisticated multiple-particle tracking algorithm of Jaqaman et al. . It works on data previously imported into an OMERO server, and produces results in the form of MATLAB data structures as well as providing functionality to visualise these results.

This simple KNIME workflow solution tracks 2D objects/cells in time series. After a few intensity based preprocessing steps, objects/cells are segmented first, then it uses Fiji TrackMate LAP method for the tracking task.

Documentation starts from p23 of the linked PDF. 

Example Image: mitocheck_small.tif (2.9M)

The Fiji distribution of ImageJ comes with several manual tracking tools, two of which are particularly useful:

* _Plugins->Tracking->Manual Tracking_

* _Plugins->Tracking->Manual tracking with TrackMate_ (TrackMate is an advanced automatic tracking tool, with the option for manual editing of tracks)

The _Manual Tracking_ plugin is quick to use, intuitive and produces easy-to-understand output. TrackMate has the advantage that automatic detection and linkage can be combined with manual input.

Update sites

MtrackJ (see the component page here) can be installed via Fiji update sites. It has many shortcut keys enabled so for manually tracking many data, it will become quite efficient as you get used to the short-cut key operation.

Pre-processing

Pre-processing steps before manual tracking might include:

* denoising and/or deconvolution

* flicker and photobleaching correction, e.g. using Fiji's _Image->Adjust->Bleach Correction_

* flat-field correction, and/or bandpass (ImageJ's _Process->FFT->Bandpass filter_) according to the size of the features of interest

This workflow is used to track multiple (appear/disappear, dividing and merging) objects in presumably big 2D+t or 3D+t datasets. It is best suitable for roundish objects or spots. Tracking is done through segmentation, which can be obtained from ilastik pixel classification, or imported from other tools. Users should provide a few object level labels, and the software predicts results on the rest of the image or new images with similar image characteristics. As a result, all objects get assigned random IDs at the first frame of the image sequence and all descendants in the same track (also children objects such as daughter cells) inherit this ID.

Tracking of focal adhesions includes a number of challenges:

1. Detection of focal adhesion regions in areas of highly variable background
2. Separation of "clumped" adhesions in different objects.
3. Dynamics: Focal adhesions dynamically, grow, shrink, change their shape, they can fuse with neighboring adhesions or one adhesion can be split into multiple children.

Würflinger et al (2011) describe how to detect focal adhesion objects and how to track them over time. Interestingly, tracking results are fed back to segmentation to improve separation of clumped adhesions.

The authors implemented the workflow in Matlab, but do not provide a ready-to-use script.