Durán-Díaz, I.; Sarmiento, A.; Fondón, I.; Bodineau, C.; Tomé, M.; Durán, R.V. A Robust Method for the Unsupervised Scoring of Immunohistochemical Staining. Entropy2024, 26, 165.
Durán-Díaz, I.; Sarmiento, A.; Fondón, I.; Bodineau, C.; Tomé, M.; Durán, R.V. A Robust Method for the Unsupervised Scoring of Immunohistochemical Staining. Entropy 2024, 26, 165.
Durán-Díaz, I.; Sarmiento, A.; Fondón, I.; Bodineau, C.; Tomé, M.; Durán, R.V. A Robust Method for the Unsupervised Scoring of Immunohistochemical Staining. Entropy2024, 26, 165.
Durán-Díaz, I.; Sarmiento, A.; Fondón, I.; Bodineau, C.; Tomé, M.; Durán, R.V. A Robust Method for the Unsupervised Scoring of Immunohistochemical Staining. Entropy 2024, 26, 165.
Abstract
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.
Keywords
Histopathological images; Principal Component Analysis; Unsupervised Stain Separation; Semi-quantitative scoring
Subject
Engineering, Telecommunications
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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