Quantification of histochemical staining by color deconvolution.

By A. Ruifrok, D. A. Johnston. Analytical and quantitative cytology and histology, 2001

2,294 citations recorded by the source corpus.

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Citations in the corpus

Poor-prognosis colon cancer is defined by a molecularly distinct subtype and develops from serrated precursor lesions (2013, methodology): “The selected tissue microarray region was then deconvoluted into a ‘blue’ hematoxylin channel and ‘brown’ 3,3′-diaminobenzidine (DAB) channel according to the algorithm proposed by Ruifrok44.”
Quantitative comparison of immunohistochemical staining measured by digital image analysis versus pathologist visual scoring (2012, methodology): “As previously described [25,26], the Color Deconvolution algorithm (Aperio) was used to isolate individual stains for quantification: the red, green, and blue (RGB) OD color vectors were measured for each stain using default software settings and control slides stained separately with hematoxylin…”
Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support (2017, unknown): “For hematoxylin and eosin (H&E) stained slides, staining normalization (SN) methods can be used [7,6].”
Deep learning-based breast cancer classification through medical imaging modalities: state of the art and research challenges (2019, methodology): “Furthermore (Zheng et al. 2017) removed the color stain by using the color deconvolution method proposed by Ruifrok and Johnston (2001).”
Cascaded discrimination of normal, abnormal, and confounder classes in histopathology: Gleason grading of prostate cancer (2012, methodology): “We can then solve C = g ( r ) M − 1 to obtain the amount of each stain present at pixel r [24].”
ImmunoRatio: a publicly available web application for quantitative image analysis of estrogen receptor (ER), progesterone receptor (PR), and Ki-67 (2010, methodology): “Analytical principle Analyzes immunostained slides (ER, PR, Ki-67) using color deconvolution [12] for stain separation and adaptive IsoData algorithm [21] for thresholding.”
Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases (2016, methodology): “…nuclei is annotated. above, with intelligently sampled challenging Using Figure 3b to identify positive pixels and the basic color deconvolution [43] thresholding approach to select random negative patches, we obtain the segmented nuclei in Figure 3d. patches for the negative class training set.”
Stacked Sparse Autoencoder (SSAE) for Nuclei Detection on Breast Cancer Histopathology Images (2015, unknown): “are described in these papers [7], [12], [15] and we direct”
New Colors for Histology: Optimized Bivariate Color Maps Increase Perceptual Contrast in Histological Images (2015, methodology): “To extract the intensity information for each stain color separately, we developed and validated an optimized variant of Ruifrok’s color deconvolution [21].”
A method for normalizing histology slides for quantitative analysis (2009, methodology): “Once the correct vectors are determined by some method, a simple color deconvolution scheme similar to [4] is used to transform the color values into quantitative values of interest:”
Methods for Nuclei Detection, Segmentation, and Classification in Digital Histopathology: A Review—Current Status and Future Potential (2014, methodology): “AMIDA benchmark reedited in 2013 the same type of mitosis detection challenge as MITOS did in 2012.”
Assessment of algorithms for mitosis detection in breast cancer histopathology images (2014, unknown): “Preprocessing : Separate hematoxylin and eosin channels were obtained with color unmixing (deconvolution) (Ruifrok and Johnston, 2001).”
Patch-based Convolutional Neural Network for Whole Slide Tissue Image Classification (2015, methodology): “This is done by decomposing the RGB color of the tissue into the H&E color space [44], followed by multiplying the magnitude of H and E of every pixel by two i.”
Automatic Nuclei Segmentation in H&E Stained Breast Cancer Histopathology Images (2013, background): “The first step is separation of the H&E stains with the color unmixing technique suggested in [20], which is a special case of true spectral unmixing techniques that work with multispectral cameras [21].”