Department of Computer Science, Chair of Computer Aided Medical Procedure, Technische Universität München, Boltzmannstr. 3, Garching 85748, Germany.
Institute of Computational Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, Institute of Computational Biology, Ingolstädter Landstraße 1, Neuherberg 85764, Germany.
Nat Commun. 2017 Jun 8;8:14836. doi: 10.1038/ncomms14836.
Quantitative analysis of bioimaging data is often skewed by both shading in space and background variation in time. We introduce BaSiC, an image correction method based on low-rank and sparse decomposition which solves both issues. In comparison to existing shading correction tools, BaSiC achieves high-accuracy with significantly fewer input images, works for diverse imaging conditions and is robust against artefacts. Moreover, it can correct temporal drift in time-lapse microscopy data and thus improve continuous single-cell quantification. BaSiC requires no manual parameter setting and is available as a Fiji/ImageJ plugin.
生物成像数据的定量分析常常受到空间阴影和时间背景变化的影响。我们引入了 BaSiC,这是一种基于低秩和稀疏分解的图像校正方法,可以解决这两个问题。与现有的阴影校正工具相比,BaSiC 用更少的输入图像实现了高精度,适用于多种成像条件,并且对伪影具有鲁棒性。此外,它还可以校正延时显微镜数据中的时间漂移,从而提高连续单细胞定量分析的准确性。BaSiC 不需要手动参数设置,并且可以作为 Fiji/ImageJ 的插件使用。