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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.
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Optical imaging is an indispensable tool in biomedical research. All modern optical imaging (whether whole slide imaging, high-content screening or high-throughput time-lapse microscopy) relies on image processing and quantification methods to analyse and interpret the acquired data. However, optical microscopy data, and especially fluorescence imaging, is often severely affected by shading or vignetting1, typically reflected as an attenuation of the brightness intensity from the centre of the optical axis to the edges. This not only degrades the visual quality of an image (for example, by causing discontinuities in whole slide images (WSIs)), but more critically compromises the downstream analysis of, for example, tissue composition or single-cell properties. Besides spatial shading effects, time-lapse movies often exhibit a temporal baseline drift due to background bleaching, which further skews the quantification of the dynamic behaviour of cellular and molecular properties2.
We propose BaSiC, a retrospective method for background and shading correction of image sequences, based on a sparse and low-rank decomposition. In comparison to existing shading correction tools, BaSiC requires fewer input images, works for diverse imaging conditions and is robust against typical image artefacts. Moreover, it can correct temporal drift for time-lapse microscopy data, and hence improve single-cell quantification. BaSiC is available as an easy-to-use Fiji/ImageJ plugin as usually requires no manual parameter tuning.
BaSiC is an efficient tool for image correction and can be applied to high-content images, WSIs and high-throughput time-lapse movies. BaSiC has immediate attraction to researchers who create stitched images, since correcting uneven illumination improves stitching and mosaic image quality. Besides, BaSiC can be also used as a pre-processing step in conjunction with automatic methods such as cell counting or measuring the morphology of cells and thus improving down-stream analysis. The crucial contribution of BaSiC is to improve intensity quantification in both static and time-lapse imaging data. Unlike local contrast equalization methods, which could distort the true intensity variations within an original image or across multiple images, BaSiC is built on solid physical models of optical imaging and hence is able to recover biologically relevant intensities for image quantification. Besides of being accurate, BaSiC is also fast to compute: in our Fiji implementation, it usually processes hundreds of images within minutes on a standard laptop.
As any shading correction method, BaSiC has limitations. One key assumption of BaSiC and all other previously mentioned multi-image based retrospective methods is that the foreground of every image to be processed should be uncorrelated with the foreground of every other image. This assumption can be violated for time-lapse movies of static and quasi-static objects, for example, for a single-cell of high-magnification that is always in the centre of the field of view. In such cases, BaSiC would consider the consistently higher image intensities in the centre of the field of view as a local increase in S(x), causing removal of the true fluorescence variability. Nevertheless in practice, BaSiC has some tolerance to such correlations, for example, it performs well in a movie of proliferating and slowly moving embryonic stem cell colonies (as shown in Supplementary Movie 2), in which consecutive frames are correlated. Meanwhile, the regularization parameter tag_hash_108s (see Methods) can be used to tune the resulting model so that it is more suitable for correlated images. Larger values of tag_hash_110s lead to a smoother estimation of the low-rank component, thus rejecting small static objects in the estimated S(x). Another useful strategy is to take samples with a large time gap in between to make images less correlated. In any case, we advise users to visually inspect the estimated shading profiles before making a correction in such challenging cases: a smooth S(x) usually indicates a good shading correction, while local inhomogeneities that come from highly corrected foreground objects are a hint of non-optimal correction.
Although BaSiC can compensate background variation, no matter if it is caused by bleaching or by switching microscopy settings, it does not account for variation in the foreground sample fluorescence that may also occur due to photobleaching. In the presented long-term single-cell time-lapse measurements, the dominant corrupting factor is the background variation caused by medium bleaching. Hence subtraction of background bleaching greatly improves the intensity quantification of single cells (as shown in Fig. 3). In fact, existing photobleaching correction methods (such as the Bleaching Correction Plugin in Fiji/ImageJ) are not suitable for correcting foreground cell bleaching in our movies: these methods either assume constant intensity or stable intensity distribution of each frame, which is certainly not the case for transcription factor expression during cell differentiation, where the signal varies depending on the cell type and time. It should also be noted that for fluorescence images, the estimated baseline can converge to the foreground, when the foreground fraction of an image is >50%. This does not affect the practical usage of BaSiC, when a high-cell density is reached only at the end of a movie. Typically then, the bleaching effect is already weak (bleaching usually decays exponentially), and hence the correction for those frames can be skipped. By contrast, for bright-field images, BaSiC is robust to different levels of cell density in background correction.
With the limitations addressed above in mind, we believe that BaSiC will help to standardize the processing and quantification of bioimage data due to its broad applicability, robust performance, elegant mathematical formulation and easy-to-use interface.
Empty-zero5 is another prospective method that approximates the flat-field S(x) as the average of images of empty images taken at various locations18. The calibration of the dark-field D(x) is same as in Calib-zero. This method is appropriate for bright-field images or fluorescence images when the medium fluoresces5 but will be not applicable for images without a medium. Both the correction of Calib-zero and Empty-zero are obtained from Smith et al.5 alongside the data.
Concentrated dye solution approximates the flat-field S using images of a thin layer of concentrated dye4. The calibration of the dark-field D(x) is same as in the above two prospective approaches. This method is usually more accurate than Calib-zero as it has a similar thickness to real specimens (Supplementary Note 1). In our study, we use the concentrated dye solution as the ground-truth to evaluate our correction of WSI.
This approach avoids the collection of reference images, yet a perfect correction for real images is impossible, as the disagreement between overlapping images includes many other sources besides uneven illumination, such as noise, alignment error, and photobleaching (the second image, (x), of the image pair usually has a lower signal than the first one, (x), due to the bleaching of fluorescence dyes even without shading). In CIDRE (ref. 5), an extra intensity normalization process is involved, which normalizes the median and the s.d. of image pair before and after correction, that is, (x), to the reference (x). However, we do not include any extra normalization process in our study, as an intensity normalization between the uncorrected pairs will affect the assessment of shading correction. Nevertheless, the scores we obtained without normalization (Fig. 2a, Supplementary Fig. 3) are very similar to those reported in CIDRE (ref. 5).
BaSiC is available as a Fiji/ImageJ Plugin from the Fiji/ImageJ update site and from our software website -muenchen.de/icb/research/groups/quantitative-single-cell-dynamics/software/basic/index.html, where we also provide five different microscopy data sets used in the manuscript to demonstrate the usage of BaSiC. See also Supplementary Note 7 for installation, usage details and practical tips.
Whole slide imaging data was acquired at the Nikon Imaging Center at the University of California, San Francisco. T.P. acknowledges a Humboldt Postdoctoral Research Fellowship. T.S. acknowledges financial support for this project from the SNF and SystemsX.ch. C.M. acknowledges support from the Deutsche Forschungsgemeinschaft (MA 5282/3-1). F.T. acknowledges funding from the Bayerisches Forschungsnetzwerk BioSysNet. T.P. and N.N. acknowledge the support of the Collaborative Research Centre SFB 824 (Z2). The authors acknowledge Kevin Smith to provide the microscopy image collection used in CIDRE5 and thank Michael K. Strasser, Felix Buggenthin, Maximilian Baust and Sailesh Conjeti for insightful discussion. 152ee80cbc
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