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SOFI评估器:一种用于SOFI数据定量质量评估的策略。

SOFIevaluator: a strategy for the quantitative quality assessment of SOFI data.

作者信息

Moeyaert Benjamien, Vandenberg Wim, Dedecker Peter

机构信息

Laboratory for NanoBiology, Department of Chemistry, KU Leuven, Celestijnenlaan 200G, 3001 Heverlee, Belgium.

出版信息

Biomed Opt Express. 2020 Jan 7;11(2):636-648. doi: 10.1364/BOE.382278. eCollection 2020 Feb 1.

Abstract

Super-resolution fluorescence imaging techniques allow optical imaging of specimens beyond the diffraction limit of light. Super-resolution optical fluctuation imaging (SOFI) relies on computational analysis of stochastic blinking events to obtain a super-resolved image. As with some other super-resolution methods, this strong dependency on computational analysis can make it difficult to gauge how well the resulting images reflect the underlying sample structure. We herein report SOFIevaluator, an unbiased and parameter-free algorithm for calculating a set of metrics that describes the quality of super-resolution fluorescence imaging data for SOFI. We additionally demonstrate how SOFIevaluator can be used to identify fluorescent proteins that perform well for SOFI imaging under different imaging conditions.

摘要

超分辨率荧光成像技术能够对超出光衍射极限的标本进行光学成像。超分辨率光学涨落成像(SOFI)依靠对随机闪烁事件进行计算分析来获取超分辨图像。与其他一些超分辨率方法一样,这种对计算分析的强烈依赖可能会使人们难以判断所得图像反映基础样本结构的程度。我们在此报告SOFIevaluator,这是一种无偏且无参数的算法,用于计算一组描述SOFI超分辨率荧光成像数据质量的指标。我们还展示了如何使用SOFIevaluator来识别在不同成像条件下适用于SOFI成像的荧光蛋白。

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