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

By Tingying Peng, Kurt S. Thorn, T. Schroeder, Lichao Wang, Fabian J Theis, Carsten Marr, Nassir Navab. Nature Communications, 2017

346 citations recorded by the source corpus.

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. Accurate quantification of bioimaging data is often confounded by uneven illumination (shading) in space and background variation in time. Here the authors present BaSiC, a Fiji plugin solving both issues. It requires fewer input images and is more robust to artefacts than existing shading correction tools.

Subfields: Biophysics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/1e4ed325b8e84811fc8397bb989e5b3ee0b52e44

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In Biophysics

Peter K. Sorger

In Molecular Biology

Citations in the corpus

Obesity shapes metabolism in the tumor microenvironment to suppress anti-tumor immunity (2020, methodology): “Raw imaging data from each of 4 CyCIF cycles were flat-field corrected using a previously described method for background and shading correction (Peng et al., 2017), then stitched and aligned using ASHLAR: a program for seamless mosaic image processing across imaging cycles (manuscript in preparatio”