Sub-diffraction error mapping for localisation microscopy images.

Sub-diffraction error mapping for localisation microscopy images.
复制标题

DOI:
10.1038/s41467-021-25812-z
复制
发表时间:
2021-09-23
影响因子:
16.6
通讯作者:
Cox S
Cox S
中科院分区:
综合性期刊1区
文献类型:
--
作者:
Marsh RJ;Costello I;Gorey MA;Ma D;Huang F;Gautel M;Parsons M;Cox S

文献摘要

参考文献

被引文献

相似文献

由于难以可靠地检测实验数据中的错误,评估定位显微图像的质量非常具有挑战性。最常见的故障模式是存在发射器重叠时定位算法产生的偏差和错误。也称为高密度或拥挤场条件,在活细胞成像中,显着的发射器重叠通常是不可避免的。在这里,我们使用哈尔小波核分析(HAWK)来生成参考图像,这是一种定位显微镜数据分析方法,已知可以产生无偏差的结果。这使得能够对除了低发射器密度数据之外的所有数据中常见的重建偏差和伪影进行映射和量化。通过避免涉及强度信息的比较,我们可以以不受定位算法中的非线性不利影响的方式映射结构伪影。纳米显微镜评估的 HAWK 方法 (HAWKMAN) 是一种通用方法,可以评估定位信息的可靠性。确定定位显微图像的质量目前具有挑战性。在此,作者报告了使用哈尔小波核分析 (HAWK) 纳米显微镜评估方法(称为 HAWKMAN)来评估定位信息的可靠性。
Assessing the quality of localisation microscopy images is highly challenging due to the difficulty in reliably detecting errors in experimental data. The most common failure modes are the biases and errors produced by the localisation algorithm when there is emitter overlap. Also known as the high density or crowded field condition, significant emitter overlap is normally unavoidable in live cell imaging. Here we use Haar wavelet kernel analysis (HAWK), a localisation microscopy data analysis method which is known to produce results without bias, to generate a reference image. This enables mapping and quantification of reconstruction bias and artefacts common in all but low emitter density data. By avoiding comparisons involving intensity information, we can map structural artefacts in a way that is not adversely influenced by nonlinearity in the localisation algorithm. The HAWK Method for the Assessment of Nanoscopy (HAWKMAN) is a general approach which allows for the reliability of localisation information to be assessed. Determining the quality of localisation microscopy images is currently challenging. Here the authors report use of the Haar wavelet kernel analysis (HAWK) Method for the Assessment of Nanoscopy, termed HAWKMAN, to assess the reliability of localisation information.
DOI: 10.1038/ncomms13558
发表时间: 2017-01-12
影响因子: 16.6
作者:
Fox-Roberts P;Marsh R;Pfisterer K;Jayo A;Parsons M;Cox S
通讯作者: Cox S
DOI: 10.1073/pnas.0907866106
发表时间: 2009-12-29
影响因子: 11.1
作者:
Dertinger, T.;Colyer, R.;Enderlein, J.
通讯作者: Enderlein, J.
DOI: 10.1038/nmeth929
发表时间: 2006-10-01
期刊: NATURE METHODS
影响因子: 48
作者:
Rust, Michael J.;Bates, Mark;Zhuang, Xiaowei
通讯作者: Zhuang, Xiaowei
DOI: 10.1126/science.1127344
发表时间: 2006-09-15
期刊: SCIENCE
影响因子: 56.9
作者:
Betzig, Eric;Patterson, George H.;Hess, Harald F.
通讯作者: Hess, Harald F.
DOI: 10.1038/s41467-019-08689-x
发表时间: 2019-02-15
影响因子: 16.6
作者:
Cohen, Edward A. K.;Abraham, Anish, V;Ober, Raimund J.
通讯作者: Ober, Raimund J.