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| 001 | 819328 | ||
| 005 | 20210129224242.0 | ||
| 024 | 7 | _ | |a 10.1038/srep33521 |2 doi |
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| 037 | _ | _ | |a FZJ-2016-05027 |
| 082 | _ | _ | |a 000 |
| 100 | 1 | _ | |a Tang, Yunqing |0 P:(DE-Juel1)156501 |b 0 |
| 245 | _ | _ | |a Automatic Bayesian single molecule identification for localization microscopy |
| 260 | _ | _ | |a London |c 2016 |b Nature Publishing Group |
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| 520 | _ | _ | |a 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). |
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| 700 | 1 | _ | |a Gensch, Thomas |0 P:(DE-Juel1)131924 |b 2 |u fzj |
| 700 | 1 | _ | |a Dai, Luru |0 P:(DE-HGF)0 |b 3 |e Corresponding author |
| 700 | 1 | _ | |a Li, Junbai |0 P:(DE-HGF)0 |b 4 |
| 773 | _ | _ | |a 10.1038/srep33521 |g Vol. 6, p. 33521 - |0 PERI:(DE-600)2615211-3 |p 33521 - |t Scientific reports |v 6 |y 2016 |x 2045-2322 |
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