1/8/2023 0 Comments Uneven contrast cellprofiler![]() dust) or changes in overall brightness of the image (Schultz et al., 1974 Regitnig et al., 2003 Piccinini et al., 2012). White-referencing approaches to illumination correction are insufficient for quantitative high-throughput microscopyįor brightfield images, dividing each image by an image of a blank field of view taken immediately after each exposure, and then normalizing the resulting image provides simple correction but is not robust against artefacts (e.g. Here, we focus on software approaches, which can further reduce intensity anomalies and improve data quality in high-throughput microscopy experiments. using fibre optics) and reducing aberrations in the optical path such as dust or nonuniform filters (due to manufacturing conditions or burn-in). Improvements to the optical path can help, such as using a light source as uniform as possible (e.g. Several approaches can mitigate this problem, also known as intensity nonuniformity, uneven shading or vignetting. However, when precise quantitative measurements are needed, variation in illumination can contribute a level of noise that confounds an experiment's goals. Uneven illumination of the field of view is often tolerable if images are analyzed qualitatively – that is, viewed by an expert. Image processing software allows the automatic, quantitative analysis of these images. This software-based solution has the potential to improve outcomes for a wide-variety of image-based HTS experiments.Īutomated microscopes have become widely used, allowing acquisition of thousands of images at rates previously unattainable. To facilitate the ready application and future development of illumination correction methods, we have made our complete test data sets as well as open-source image analysis pipelines publicly available. We find that applying the proposed post-hoc correction method improves performance in both experiments, even when illumination correction has already been applied using software associated with the instrument. We test the pipeline on real-world high-throughput image sets and evaluate the performance of the pipeline at two levels: (a) Z′-factor to evaluate the effect of the image correction on a univariate readout, representative of a typical high-content screen, and (b) classification accuracy on phenotypic signatures derived from the images, representative of an experiment involving more complex data mining. We present a straightforward illumination correction pipeline that has been used by our group across many experiments. In this paper, we seek to quantify the improvement in the quality of high-content screen readouts due to software-based illumination correction. This often adds an unacceptable level of noise, obscures true quantitative differences and precludes biological experiments that rely on accurate fluorescence intensity measurements. Among the most common sources of systematic noise is non-homogeneous illumination across the image field. Scale bars = 15 μm.The presence of systematic noise in images in high-throughput microscopy experiments can significantly impact the accuracy of downstream results. ![]() Cells touching the border are intentionally excluded from analysis and images were contrast stretched for display. (e) Outlines show the identification of nuclei and identification of cell edges made by CellProfiler in human HT29 (left) and Drosophila Kc167 (right) cells. The results are comparable to those produced by white referenced images (right), but they do not require the error prone and often omitted step of collecting a white reference image immediately before image acquisition. (d) These corrections reduce noise in quantitative measurements, demonstrated here in DNA content measures (middle) from images of Drosophila Kc167 cells that are improved over the raw images (left). Images were contrast-enhanced to display this effect. CellProfiler's illumination correction modules correct these anomalies (right). (c) Image processing example: uneven illumination from the left to the right within each field of view is noticeable in this three row by five column tiled image (left). (b) Schematic of a typical CellProfiler pipeline. ![]() (a) Main CellProfiler interface, with an analysis pipeline displayed.
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