Short Description
EMC2 plugin & block(s) is designed for closing tracking gaps in single-particle tracking. It is particularly well-suited for monitoring single neuron activity in calcium imaging of behaving animals.
Institution: Institut Pasteur
Website: https://research.pasteur.fr/fr/team/bioimage-analysis/
Documentation
EMC2 plugin is dedicated to the robust tracking of particles with intermittent detectability in moving and deforming environments. It is specifically designed for the long term monitoring of single neuron activity in behaving animals.
A Tutorial for using EMC2 protocol for the tracking of neurons in calcium imaging of behaving animals can be found here: S1_text
.tif movies used in the original manuscript can be downloaded from the Biostudies website: https://www.ebi.ac.uk/biostudies/studies/S-BSST428
The main input of the plugin is a collection of tracks obtained with standard single-particle-tracking algorithms such as the enhanced Multiple-Hypothesis-Algorithm (eMHT) that is already implemented in Icy with the plugin Spot Tracking (https://icy.bioimageanalysis.org/plugin/spot-tracking/). Considering that spots are embedded in the field of view (e.g. neurons in deforming animal), the plugin uses the information contained in input tracks about the deformation of the field of view to compute the elastic deformation of the field-of-view. After having corrected for the deformation of the field of view, short tracks are linked (i.e. tracking gaps are closed) by minimizing the global distance between the end-points of prematurely terminated tracks with the starting-points of newly appearing tracks. The output of EMC2 plugin is concatenated short tracks, i.e. to tracks where tracking gaps have been closed thanks to the estimation of the elastic deformation of the field of view.
Main plugin: EMC2
Main block: EMC2 block
Input description
Tracks : the collection of (shortened) tracks computed with Spot tracking plugin (https://icy.bioimageanalysis.org/plugin/spot-tracking/). These tracks will be used to iteratively compute the elastic deformation of the field of view, and they will be further concatenated to close long tracking gaps (corresponding to undetected particles)
Max. number of fiducials : Maximum number of tracks used as fiducial points for the iterative computation of the elastic transformation of the field of view between consecutive time frames (algorithm is based on thin-plate-spline interpolation between fiducials : Chui, H., & Rangarajan, A. (2000, June). A new algorithm for non-rigid point matching. In Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No. PR00662) (Vol. 2, pp. 44-51). IEEE.).
Max. distance for track concatenation : To close tracking gaps between input (short) tracks, and after having corrected for the motion and deformation of the field of view with thin-plate-spline interpolations, EMC2 algorithm uses a global distance minimization method to link ending points of prematurely terminated (input) tracks with their counterparts, the starting points of newly created tracks when spots (e.g. neurons) are re-detectable. “Max. distance for track concatenation” input parameter defines the maximum acceptable distance to link ending- and starting-points of input tracks. This parameter aims at avoiding “over-linking” of tracks and is a user-defined parameter.
Max. time window for track concatenation : This input parameter is similar to the “Max. distance for track concatenation” parameter and set a maximum length for the tracking gaps to be closed: if the time lapse between a track ending-point and a track starting-point exceed this maximum time window, they can’t be linked (concatenated). Again, this parameter prevents an over-linking of tracks and also, reduce the linking errors because of the error propagation during the iterative estimation of the motion/deformation of the field-of-view.
Alternative cost factor (for JV linear association): The Jonker-Volgenant algorithm for computing the optimal linear association between ending- and starting- points of input tracks is much faster than the exact Hungarian algorithm (Kuhn, H. W. (1955). The Hungarian method for the assignment problem. Naval research logistics quarterly, 2(1‐2), 83-97.) but uses an approximate greedy association for very close ending- and starting-points. The value of the alternative cost factor define the minimal distance under which the points are automatically associated. Therefore, the higher is the alternative cost factor, the higher is the computational cost, and the more accurate is the solution of the linear association problem.
No motion correction: This boolean input can be used to measure how (bad) would be the tracks’ concatenation if there was no correction for the deformation of the field-of-view.
