Live sample imaging

Object tracking. For each time-frame, an image mask is obtained from median filtering (user defined radius), thresholding (user defined level) and hole filling. Convex objects are split apart by distance map watershed from regional intensity maxima (user defined noise tolerance), eroded (user defined radius) and analyzed as 3D particles (assuming some overlap between objects from a frame to the next frame). Finally, division events are analyzed and accounted for to relabel objects.

TGMM is a cell tracking solution for large 3D volume (typically lightsheet).

It detects cell nuclei by fitting gaussians on their fluorescent intensity.

It can run on GPU using CUDA and is called via the command line.

OligoMacro Toolset, is an ImageJ macro-toolset aimed at isolating oligodendrocytes from wide-field images, tracking isolated cells, characterizing processes morphology along time, outputting numerical data and plotting them. It takes benefit of ImageJ built-in functions to process images and extract data, and relies on the R software in order to generate graphs.

Set of Tools for super resolution microscopy

SIMPLETRACKER a simple particle tracking algorithm that can deal with gaps.

Tracking , or particle linking, consist in re-building the trajectories of one or several particles as they move along time. Their position is reported at each frame, but their identity is yet unknown: we do not know what particle in one frame corresponding to a particle in the previous frame. Tracking algorithms aim at providing a solution for this problem. 

simpletracker.m is - as the name says - a simple implementation of a tracking algorithm, that can deal with gaps. A gap happens when one particle that was detected in one frame is not detected in the subsequent one. If not dealt with, this generates a track break, or a gap, in the frame where the particle disappear, and a false new track in the frame where it re-appear.