#Particle tracker imagej software#

The file specifies (in XML terms) the position (x,y,z) of each particle at each relevant time point (t). Each training set (that is, a given scenario, SNR level, and particle density level) has a separate XML file associated with it. The ground truth particle positions and tracks in the training image data are provided in XML format as in the example below.

Densities ĭownload the images and ground truth tracks from this scenario here.

Dynamics: same direction, switch between brownian and linear.

Their exact size (relative to pixel size) and numbers, their dynamics, as well as their start and end frames (in time), are slightly randomized to mimic reality, in which case these values are not known exactly, either.Ī brief description of the image data for the four scenarios: As for density, we defined three categories, depending on the scenario: low density (on the order of 50-100 particles), mid density (100-500 particles), and high density (on the order of 1000 particles or even more). Note that a given SNR indicates the maximum SNR in that data (it drops as particles move out of focus). We selected this level, and added one higher level (SNR=7), one lower level (SNR=2), and one very low level (SNR=1). As indicated in several papers in the literature, SNR=4 is a critical level at which methods may start to break down.

For each of these scenarios, image data were generated at four different SNR levels (Poisson noise was used as this is the limiting case in microscopy), and of three density levels. To make the challenge feasible, we focus on three key aspects affecting the performance of particle tracking algorithms: (1) different particle imaging scenarios, (2) different signal-to-noise ratios (SNR) of the data, and (3) different levels of particle density.įour different scenarios are selected for the challenge, mimicking real image data of moving viruses, vesicles, receptors, and microtubule tips. Needless to say, the space spanned by these variables is very high-dimensional, and would require a very large set of sample image data to explore. In practice, there are many variables (microscope settings, particle properties, image parameters) that could be varied, and for which the performance of tracking methods could be evaluated. This requires simulation of particle imaging to a hopefully good level of realism.

#Particle tracker imagej software#

Software list to generate, display and score tracks and scenarios.Īs announced, the particle tracking challenge are based on computer generated data only, in order to allow accurate, quantitative performance evaluation of all methods under controlled conditions, with hard and objective ground truth. Material for the Particle Tracking Challenge Content In no event shall the challenge organizers be liable to any party for direct, indirect, special, indicental, or consequential damages, of any kind whatsoever, arising out of the use of any of the material on this page, even if advised of the possibility thereof. It is not allowed to redistribute, sell, or lease the material or derivative works thereof. Permission to use the material on this page for educational, research, and not-for-profit purposes, is granted without a fee and without a signed licensing agreement. van Wezel, Han-Wei Dan, Yuh-Show Tsai, Carlos Ortiz de Solórzano, Jean-Christophe Olivo-Marin, Erik Meijering. Shorte, Joost Willemse, Katherine Celler, Gilles P. Blau, Perrine Paul-Gilloteaux, Philippe Roudot, Charles Kervrann, François Waharte, Jean-Yves Tinevez, Spencer L.

Godinez, Karl Rohr, Yannis Kalaidzidis, Liang Liang, James Duncan, Hongying Shen, Yingke Xu, Klas E.

Sbalzarini, Yuanhao Gong, Janick Cardinale, Craig Carthel, Stefano Coraluppi, Mark Winter, Andrew R. Nicolas Chenouard, Ihor Smal, Fabrice de Chaumont, Martin Maška, Ivo F. Objective comparison of particle tracking methods If you publish results based on any of the material presented on this page we expect you to acknowledge our work by citing the following paper: