A BaSiC tool for background and shading correction of optical microscopy images

Tingying Peng,Fabian J Theis,Timm Schroeder,Lichao Wang,Nassir Navab,Carsten Marr,Kurt Thorn

doi:10.1038/ncomms14836

Abstract

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.

Highlights

Background bleachingTime due to imperfect filtering, which are difficult to measure experimentally and difficult to correct with prospective methods (Supplementary Fig. 4a)
We propose BaSiC, a retrospective method for background and shading correction of image sequences, based on a sparse and low-rank decomposition
We subsequently evaluate BaSiC using a comprehensive microscope image collection provided by Smith et al.[5], which includes 10 real microscopy data sets, one photography data set and one synthetic microscopy data set

Summary

Introduction

Time due to imperfect filtering, which are difficult to measure experimentally and difficult to correct with prospective methods (Supplementary Fig. 4a). BaSiC incorporates such artefacts in the estimation of D(x) and can successfully correct their effect (Supplementary Fig. 4b). A quantitative evaluation of 45 WSIs using the estimation score suggests that BaSiC achieves an accurate estimation of shading in all instances, outperforming existing retrospective methods (Fig. 2e, Supplementary Figs 5–8). BaSiC corrects background variation in time-lapse movies. We apply BaSiC to improve single-cell quantification of long-term time-lapse microscopy. We decompose the shadingfree true image Iitrue(x) of the ith frame of a time-lapse microscopy movie into the sum of a spatially-constant baseline signal, Bi, and the spatially varying foreground (fluorescence) signal of biological relevance[6]. The full model for a time-lapse movie becomes: IimeasðxÞ1⁄4ðBi þ FiðxÞÞÂSðxÞ þ DðxÞ ð2Þ

Methods

Results

Conclusion