The datasets simulate biological samples that are imaged by a microscope with an EMCCD camera. Different structures have been generated to produce many experimental scenarios that vary from ideal acquisition scenarios to highly perturbed scenarios. Two categories have been proposed:
Sample factory: Biological sample are composed of extruded tubes that simulate microtubules (around diameter 25 nm). The tubes are defined as 3D geometric shapes in the continuous-spatial domain, allowing for arbitrarily high precision of the ground-truth data. For instance, the central axis of the tubes is parameterized by a set of cubic spline knots.
Fluorophores factory: The sample compounds are decorated with hundreds of thousands of fluorophores according to a specified density for each compound. Every fluorophore has an independent photoactivation behavior: it can be switched on at random times and emit a random quantity of photons according to a lifetime model (constant emission, exponential-decay emission). The fluorophore positions are stored as double precision lists.
Frame Sequence factory: The whole acquisition chain is simulated. Starting at a given time and for a specified frame rate, it computes thousands of synthetic images containing only the active fluorophores. The computation is performed at high resolution (5 nm/pixel) then the images are down-sampled to a camera resolution (100 nm/pixel or 150 nm/pixel). The simulation includes the following acquisition perturbations:
For the challenge, the number of pertubations are limited to those that directly related to the localization procedure: 1) 2D localization, recover the lateral positions (XY), 2) flat sample (less than 1 μm), 3) no drift 4) a single channel, 5) low density of fluorophores or high density of fluorophores per volume unit.
The results of the localization are given in a delimiter-separated values text file. Every localization position of each frame is stored as row in this file. The description file is a XML that helps to decode the localization file format and it gives the spatial reference allowing comparaison.
Required information for each row
The PALM-siever platform covers both visualization and analysis of single-molecule localization microscopy data. Built on MATLAB, it enables data to be modified and displayed interactively.
Biomedical Imaging Group, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
Last update: 30 Nov 2018