Automatic Particle Detection in Microscopy Using Temporal Correlations
Artikel i vetenskaplig tidskrift, 2013

One of the fundamental problems in the analysis of single particle tracking data is the detection of individual particle positions from microscopy images. Distinguishing true particles from noise with a minimum of false positives and false negatives is an important step that will have substantial impact on all further analysis of the data. A common approach is to obtain a plausible set of particles from a larger set of candidate particles by filtering using manually selected threshold values for intensity, size, shape, and other parameters describing a particle. This introduces subjectivity into the analysis and hinders reproducibility. In this paper, we introduce a method for automatic selection of these threshold values based on maximizing temporal correlations in particle count time series. We use Markov Chain Monte Carlo to find the threshold values corresponding to the maximum correlation, and we study several experimental data sets to assess the performance of the method in practice by comparing manually selected threshold values from several independent experts with automatically selected threshold values. We conclude that the method produces useful results, reducing subjectivity and the need for manual intervention, a great benefit being its easy integratability into many already existing particle detection algorithms.

image analysis

unsupervised learning

optical microscopy

fluorescence microscopy

Författare

Magnus Röding

Göteborgs universitet

Chalmers, Matematiska vetenskaper, matematisk statistik

SuMo Biomaterials

Hendrik Deschout

Universiteit Gent

Thomas Martens

Universiteit Gent

Kristof Notelaers

Universiteit Hasselt

KU Leuven

Johan Hofkens

KU Leuven

M. Ameloot

Universiteit Hasselt

Kevin Braeckmans

Universiteit Gent

Aila Särkkä

Göteborgs universitet

Chalmers, Matematiska vetenskaper, matematisk statistik

Mats Rudemo

Chalmers, Matematiska vetenskaper, matematisk statistik

Göteborgs universitet

Microscopy Research and Technique

1059-910X (ISSN)

Vol. 76 997-1006

Ämneskategorier

Biologiska vetenskaper

DOI

10.1002/jemt.22260