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@ARTICLE{Tang:819328,
author = {Tang, Yunqing and Hendriks, Johnny and Gensch, Thomas and
Dai, Luru and Li, Junbai},
title = {{A}utomatic {B}ayesian single molecule identification for
localization microscopy},
journal = {Scientific reports},
volume = {6},
issn = {2045-2322},
address = {London},
publisher = {Nature Publishing Group},
reportid = {FZJ-2016-05027},
pages = {33521 -},
year = {2016},
abstract = {Single molecule localization microscopy (SMLM) is on its
way to become a mainstream imaging technique in the life
sciences. However, analysis of SMLM data is biased by user
provided subjective parameters required by the analysis
software. To remove this human bias we introduce here the
Auto-Bayes method that executes the analysis of SMLM data
automatically. We demonstrate the success of the method
using the photoelectron count of an emitter as selection
characteristic. Moreover, the principle can be used for any
characteristic that is bimodally distributed with respect to
false and true emitters. The method also allows generation
of an emitter reliability map for estimating quality of
SMLM-based structures. The potential of the Auto-Bayes
method is shown by the fact that our first basic
implementation was able to outperform all software packages
that were compared in the ISBI online challenge in 2015,
with respect to molecule detection (Jaccard index).},
cin = {ICS-4},
ddc = {000},
cid = {I:(DE-Juel1)ICS-4-20110106},
pnm = {552 - Engineering Cell Function (POF3-552)},
pid = {G:(DE-HGF)POF3-552},
typ = {PUB:(DE-HGF)16},
UT = {WOS:000383385300001},
pubmed = {pmid:27641933},
doi = {10.1038/srep33521},
url = {https://juser.fz-juelich.de/record/819328},
}