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DOI: 10.1055/s-0043-1768067
Color Deconvolution as a Simple and Rapid Tool in Quantitative Biomedical Research
The quality, efficiency, and speed of a quantitative analysis are critical factors in cytopathology. In this scenario, reliable and reproducible tools are needed to improve results in a shorter period,[1] mainly routine immunohistochemistry (IHC) slides and biomedical research.
Histological experiments rely on visualization of results using staining techniques, due to the ability of light-absorbing dyes to selectively bind to molecules and complexes of interest, which may provide a quantitative analysis when combining computational techniques.[2] Thereby, color deconvolution can be addressed as a versatile tool in quantitative analysis as this method is able to split in channels the different dyes of a staining technique,[3] which allows analysis of the area fraction of the aimed structures.
In [Fig. 1], we can see that the “color deconvolution” tool on ImageJ (National Institutes of Health, United States) allows unmixing brightfield images into channels representing the absorbance of the individual dyes. After splitting the channels, images can be turned into gray with the aid of the “threshold” tool to determine the structure area. Then, we can measure the “area fraction” of the stained structures in contrast with the white background. Using this method, it is possible to quantify the stained area of each field in a semi-automatic manner, which allows a greater flow of analysis.
This simple and rapid technique to analyze the absorbance of different dyes in a quantitative manner has the potential to increase the flow of analysis in biomedical research. To corroborate with our presented toolkit, color deconvolution was previously used by researchers to study, e.g., hepatocellular carcinoma,[4] atherosclerotic lesions,[5] deep neural networks,[6] and skin layers.[7] These studies showed the versatility of this technique in both histochemistry and IHC as the brown color generated by 3,3′-diaminobenzidine from IHC can be separate from the original image and quantitatively analyzed to show the percentage of its stained structure. Additionally, this process of analysis can be addressed in the manuscripts as a supplementary material to show the image after color deconvolution.
The growing availability of image digitization and bioinformatic technologies has driven the search for new ways of analyzing image datasets.[2] [8] [9] Thus, to have a reliable method to increase the speed of analysis is of paramount importance to conduct cytological studies in larger samples. In this sense, we hope that this toolkit may be a useful method in further histopathological studies.
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Conflict of Interest
None declared.
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References
- 1 Mandarim-de-Lacerda CA. Stereological tools in biomedical research. An Acad Bras Cienc 2003; 75 (04) 469-486
- 2 Shu J, Dolman GE, Duan J, Qiu G, Ilyas M. Statistical colour models: an automated digital image analysis method for quantification of histological biomarkers. Biomed Eng Online 2016; 15: 46
- 3 Landini G, Martinelli G, Piccinini F. Colour deconvolution: stain unmixing in histological imaging. Bioinformatics 2021; 37 (10) 1485-1487
- 4 Zhou S, Parham DM, Yung E, Pattengale P, Wang L. Quantification of glypican 3, β-catenin and claudin-1 protein expression in hepatoblastoma and paediatric hepatocellular carcinoma by colour deconvolution. Histopathology 2015; 67 (06) 905-913
- 5 Chen Y, Yu Q, Xu CB. A convenient method for quantifying collagen fibers in atherosclerotic lesions by ImageJ software. Int J Clin Exp Med 2017; 10 (10) 14904-14910
- 6 Lahiani A, Gildenblat J, Klaman I, Navab N, Klaiman E. Generalising multistain immunohistochemistry tissue segmentation using end-to-end colour deconvolution deep neural networks. IET Image Process 2019; 13 (07) 1066-1073
- 7 Hussein S. Automatic layer segmentation in H&E images of mice skin based on colour deconvolution and fuzzy C-mean clustering. Inform Med Unlocked 2021; 25: 100692
- 8 Haub P, Meckel T. A model based survey of colour deconvolution in diagnostic brightfield microscopy: Error estimation and spectral consideration. Sci Rep 2015; 5: 12096
- 9 Alsubaie N, Trahearn N, Raza SEA, Snead D, Rajpoot NM. Stain deconvolution using statistical analysis of multi-resolution stain colour representation. PLoS One 2017; 12 (01) e0169875
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Publication History
Article published online:
24 April 2023
© 2023. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/)
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References
- 1 Mandarim-de-Lacerda CA. Stereological tools in biomedical research. An Acad Bras Cienc 2003; 75 (04) 469-486
- 2 Shu J, Dolman GE, Duan J, Qiu G, Ilyas M. Statistical colour models: an automated digital image analysis method for quantification of histological biomarkers. Biomed Eng Online 2016; 15: 46
- 3 Landini G, Martinelli G, Piccinini F. Colour deconvolution: stain unmixing in histological imaging. Bioinformatics 2021; 37 (10) 1485-1487
- 4 Zhou S, Parham DM, Yung E, Pattengale P, Wang L. Quantification of glypican 3, β-catenin and claudin-1 protein expression in hepatoblastoma and paediatric hepatocellular carcinoma by colour deconvolution. Histopathology 2015; 67 (06) 905-913
- 5 Chen Y, Yu Q, Xu CB. A convenient method for quantifying collagen fibers in atherosclerotic lesions by ImageJ software. Int J Clin Exp Med 2017; 10 (10) 14904-14910
- 6 Lahiani A, Gildenblat J, Klaman I, Navab N, Klaiman E. Generalising multistain immunohistochemistry tissue segmentation using end-to-end colour deconvolution deep neural networks. IET Image Process 2019; 13 (07) 1066-1073
- 7 Hussein S. Automatic layer segmentation in H&E images of mice skin based on colour deconvolution and fuzzy C-mean clustering. Inform Med Unlocked 2021; 25: 100692
- 8 Haub P, Meckel T. A model based survey of colour deconvolution in diagnostic brightfield microscopy: Error estimation and spectral consideration. Sci Rep 2015; 5: 12096
- 9 Alsubaie N, Trahearn N, Raza SEA, Snead D, Rajpoot NM. Stain deconvolution using statistical analysis of multi-resolution stain colour representation. PLoS One 2017; 12 (01) e0169875