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题名: Automatic Bayesian single molecule identification for localization microscopy
作者: Tang, Yunqing1; Hendriks, Johnny2; Gensch, Thomas2; Dai, Luru1; Li, Junbai1, 3
刊名: SCIENTIFIC REPORTS
发表日期: 2016-09-19
卷: 6
收录类别: SCI
英文摘要: 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).
语种: 英语
内容类型: 期刊论文
URI标识: http://ir.iccas.ac.cn/handle/121111/35246
Appears in Collections:胶体、界面与化学热力学实验室_期刊论文

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作者单位: 1.Natl Ctr Nanosci & Technol China, Beijing 100190, Peoples R China
2.Forschungszentrum Julich, Inst Complex Syst ICS 4, Cellular Biophys, D-52428 Julich, Germany
3.Chinese Acad Sci, Inst Chem, Beijing 100190, Peoples R China
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