Massively parallel approximate Bayesian computation for estimating nanoparticle diffusion coefficients, sizes and concentrations using confocal laser scanning microscopy
Journal article, 2018

We implement a massively parallel population Monte Carlo approximate Bayesian computation (PMC‐ABC) method for estimating diffusion coefficients, sizes and concentrations of diffusing nanoparticles in liquid suspension using confocal laser scanning microscopy and particle tracking. The method is based on the joint probability distribution of diffusion coefficients and the time spent by a particle inside a detection region where particles are tracked. We present freely available central processing unit (CPU) and graphics processing unit (GPU) versions of the analysis software, and we apply the method to characterize mono‐ and bidisperse samples of fluorescent polystyrene beads.

Confocal laser scanning microscopy

Particle tracking


Diffusion coefficient



Magnus Röding

RISE Research Institutes of Sweden

Markus Billeter

Chalmers, Space, Earth and Environment, Astronomy and Plasmaphysics

Journal of Microscopy

0022-2720 (ISSN) 1365-2818 (eISSN)

Vol. 271 2 174-182

Subject Categories

Computer Engineering

Biomedical Laboratory Science/Technology

Computer Vision and Robotics (Autonomous Systems)

Medical Image Processing


C3SE (Chalmers Centre for Computational Science and Engineering)



More information

Latest update

3/2/2021 2