Single-Molecule Localization Microscopy  •  Software Benchmarking

Methodology

Benchmarking of the localization is based on a solid methodology that implies a construction of realistic bio-inspired datasets and scientific metrics for the assessment.

Practical issues has to take into account like the file format and the rendering.

banner

Realistic simulation of datasets

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.

Metrics of assessment

Towards standardization of the file format

Interactive Creation of the XML Description File

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

  • Mandatory - Frame number starting from 1
  • Mandatory - Position in X axis (in nm)
  • Mandatory - Position in Y axis (in nm)
  • Optional - Position in Z axis (in nm)
  • Optional - Measured intensity
  • Optional - Confidence in the measurement (%)

Description XML File

Organization
Separator of columns Required
Unit for the XY position default in nm
Row of the first localization
 
Column index • Important note: column index starts at 0
Column of the frame Required
Column for the X position Required
Column for the Y position Required
Column for the Z position Optional
Column for the intensity Optional
Column for the confidence (%) Optional
 
Shift of the origin • Check the figure
X shift default value: 0
Y shift default value: 0
Z shift default value: 0
Unit shift default in pixel
 
Shift in the numbering of the frames • Convention: 1 for the first frame
Frame shift default value: 0

Predefined settings

Rendering

PALM-siever • Visualization for Single-Molecule Localization Microscopy

palm-siever

For this challenge, we use PALM-siever as tool to render the reconstruction image from the localization results. PALM-siever is hosted by Google Project.

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.

Features

© 2018 Biomedical Imaging Group, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
Last update: 30 Nov 2018