DAB-quant: An open-source digital system for quantifying immunohistochemical staining with 3,3′-diaminobenzidine (DAB)

Patel, Sneh; Fridovich-Keil, Sara; Rasmussen, Shauna A.; Fridovich‐Keil, Judith L.

doi:10.1371/journal.pone.0271593

Cited by 2 publications

(1 citation statement)

References 13 publications

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“…Although some platforms and applications for the semiquantitative scoring of IHC images already exist, they require reference images as well as the intervention of researchers. Examples of these systems are QuPath [10], IHC Profiler [11] and DAB-quant [12].…”

Section: Introductionmentioning

confidence: 99%

A Robust Method for the Unsupervised Scoring of Immunohistochemical Staining

Durán-Díaz,

Sarmiento,

Fondón

et al. 2024

Entropy

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Immunohistochemistry is a powerful technique that is widely used in biomedical research and clinics; it allows one to determine the expression levels of some proteins of interest in tissue samples using color intensity due to the expression of biomarkers with specific antibodies. As such, immunohistochemical images are complex and their features are difficult to quantify. Recently, we proposed a novel method, including a first separation stage based on non-negative matrix factorization (NMF), that achieved good results. However, this method was highly dependent on the parameters that control sparseness and non-negativity, as well as on algorithm initialization. Furthermore, the previously proposed method required a reference image as a starting point for the NMF algorithm. In the present work, we propose a new, simpler and more robust method for the automated, unsupervised scoring of immunohistochemical images based on bright field. Our work is focused on images from tumor tissues marked with blue (nuclei) and brown (protein of interest) stains. The new proposed method represents a simpler approach that, on the one hand, avoids the use of NMF in the separation stage and, on the other hand, circumvents the need for a control image. This new approach determines the subspace spanned by the two colors of interest using principal component analysis (PCA) with dimension reduction. This subspace is a two-dimensional space, allowing for color vector determination by considering the point density peaks. A new scoring stage is also developed in our method that, again, avoids reference images, making the procedure more robust and less dependent on parameters. Semi-quantitative image scoring experiments using five categories exhibit promising and consistent results when compared to manual scoring carried out by experts.

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Section: Introductionmentioning

confidence: 99%

A Robust Method for the Unsupervised Scoring of Immunohistochemical Staining

Durán-Díaz,

Sarmiento,

Fondón

et al. 2024

Entropy

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Robust Method for Unsupervised Scoring of Inmunohistoquemical Staining

Durán-Díaz,

Sarmiento,

Fondón

et al. 2023

Preprint

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Immunohistochemistry is a powerful technique, widely used in biomedical research and clinics, that allows to determine the expression levels of some proteins of interest in tissue samples by means of the intensity of color due to the expression of biomarkers using specific antibodies. As such, immunohistochemical images are complex and problematic to be quantified. Recently we proposed a novel method including a first separation stage based on nonnegative matrix factorization (NMF) that achieved good results. However, that method was highly dependent on sparseness and non-negativity parameter choice and on algorithm initialization. Furthermore, the previously proposed method needed a reference image as starting point for the NMF algorithm. In the present work, we propose a new, simpler and robust method for the automated unsupervised scoring of immunohistochemical images based on bright field. Our work is focused on images from tumor tissues marked with blue (nuclei) and brown (protein of interest). The new proposed method represents a simpler approach that, in one side, avoids the use of NMF for the separation stage, and in the other side, circumvents the need for a control image. This new approach determines the subspace spanned by the two colors of interest by means of principal component analysis (PCA) with dimension reduction. This subspace is a two-dimensional space, allowing the finding of color vectors by considering the peaks of density of points. The method also develops a new scoring stage by avoiding, again, reference images, making the procedure more robust and less dependent on the parameters. Experiments for the semi-quantitative scoring of images in five categories exhibit promising and consistent results when compared to manual scoring carried out by experts.

show abstract

DAB-quant: An open-source digital system for quantifying immunohistochemical staining with 3,3′-diaminobenzidine (DAB)

Cited by 2 publications

References 13 publications

A Robust Method for the Unsupervised Scoring of Immunohistochemical Staining

A Robust Method for the Unsupervised Scoring of Immunohistochemical Staining

Robust Method for Unsupervised Scoring of Inmunohistoquemical Staining

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