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New Colors for Histology: Optimized Bivariate Color Maps Increase Perceptual Contrast in Histological Images.

Kather JN, Weis CA, Marx A, Schuster AK, Schad LR, Zöllner FG - PLoS ONE (2015)

Bottom Line: To validate whether this procedure improves distinguishability of objects and background in histological images, we re-stain phantom images and N = 596 large histological images of immunostained samples of human solid tumors.We show that perceptual contrast is improved by a factor of 2.56 in phantom images and up to a factor of 2.17 in sets of histological tumor images.Thus, we provide an objective and reliable approach to measure object distinguishability in a given histological image and to maximize visual information available to a human observer.

View Article: PubMed Central - PubMed

Affiliation: Institute of Pathology, University Medical Center Mannheim, University of Heidelberg, Mannheim, Germany.

ABSTRACT

Background: Accurate evaluation of immunostained histological images is required for reproducible research in many different areas and forms the basis of many clinical decisions. The quality and efficiency of histopathological evaluation is limited by the information content of a histological image, which is primarily encoded as perceivable contrast differences between objects in the image. However, the colors of chromogen and counterstain used for histological samples are not always optimally distinguishable, even under optimal conditions.

Methods and results: In this study, we present a method to extract the bivariate color map inherent in a given histological image and to retrospectively optimize this color map. We use a novel, unsupervised approach based on color deconvolution and principal component analysis to show that the commonly used blue and brown color hues in Hematoxylin-3,3'-Diaminobenzidine (DAB) images are poorly suited for human observers. We then demonstrate that it is possible to construct improved color maps according to objective criteria and that these color maps can be used to digitally re-stain histological images.

Validation: To validate whether this procedure improves distinguishability of objects and background in histological images, we re-stain phantom images and N = 596 large histological images of immunostained samples of human solid tumors. We show that perceptual contrast is improved by a factor of 2.56 in phantom images and up to a factor of 2.17 in sets of histological tumor images.

Context: Thus, we provide an objective and reliable approach to measure object distinguishability in a given histological image and to maximize visual information available to a human observer. This method could easily be incorporated in digital pathology image viewing systems to improve accuracy and efficiency in research and diagnostics.

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Related in: MedlinePlus

Digitally re-stained images of Ki67 stained samples.Two representative images from set ‘MKI67-uro’ (Ki67 in urothelial cancer). (A, C): original images, (B, D): re-stained images in red (#FF0000)—blue (#0093FF). It can be seen that the contrast of foreground (i.e. Ki67 positive cells) to background is improved after re-staining.
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pone.0145572.g006: Digitally re-stained images of Ki67 stained samples.Two representative images from set ‘MKI67-uro’ (Ki67 in urothelial cancer). (A, C): original images, (B, D): re-stained images in red (#FF0000)—blue (#0093FF). It can be seen that the contrast of foreground (i.e. Ki67 positive cells) to background is improved after re-staining.

Mentions: Finally, we used the described method to re-stain 8 sets of histological images of different human solid tumors stained for clinically relevant markers. In Fig 6, two examples from the set ‘MKI67-uro’ (Ki67 in urothelial cancer) are shown.


New Colors for Histology: Optimized Bivariate Color Maps Increase Perceptual Contrast in Histological Images.

Kather JN, Weis CA, Marx A, Schuster AK, Schad LR, Zöllner FG - PLoS ONE (2015)

Digitally re-stained images of Ki67 stained samples.Two representative images from set ‘MKI67-uro’ (Ki67 in urothelial cancer). (A, C): original images, (B, D): re-stained images in red (#FF0000)—blue (#0093FF). It can be seen that the contrast of foreground (i.e. Ki67 positive cells) to background is improved after re-staining.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4696851&req=5

pone.0145572.g006: Digitally re-stained images of Ki67 stained samples.Two representative images from set ‘MKI67-uro’ (Ki67 in urothelial cancer). (A, C): original images, (B, D): re-stained images in red (#FF0000)—blue (#0093FF). It can be seen that the contrast of foreground (i.e. Ki67 positive cells) to background is improved after re-staining.
Mentions: Finally, we used the described method to re-stain 8 sets of histological images of different human solid tumors stained for clinically relevant markers. In Fig 6, two examples from the set ‘MKI67-uro’ (Ki67 in urothelial cancer) are shown.

Bottom Line: To validate whether this procedure improves distinguishability of objects and background in histological images, we re-stain phantom images and N = 596 large histological images of immunostained samples of human solid tumors.We show that perceptual contrast is improved by a factor of 2.56 in phantom images and up to a factor of 2.17 in sets of histological tumor images.Thus, we provide an objective and reliable approach to measure object distinguishability in a given histological image and to maximize visual information available to a human observer.

View Article: PubMed Central - PubMed

Affiliation: Institute of Pathology, University Medical Center Mannheim, University of Heidelberg, Mannheim, Germany.

ABSTRACT

Background: Accurate evaluation of immunostained histological images is required for reproducible research in many different areas and forms the basis of many clinical decisions. The quality and efficiency of histopathological evaluation is limited by the information content of a histological image, which is primarily encoded as perceivable contrast differences between objects in the image. However, the colors of chromogen and counterstain used for histological samples are not always optimally distinguishable, even under optimal conditions.

Methods and results: In this study, we present a method to extract the bivariate color map inherent in a given histological image and to retrospectively optimize this color map. We use a novel, unsupervised approach based on color deconvolution and principal component analysis to show that the commonly used blue and brown color hues in Hematoxylin-3,3'-Diaminobenzidine (DAB) images are poorly suited for human observers. We then demonstrate that it is possible to construct improved color maps according to objective criteria and that these color maps can be used to digitally re-stain histological images.

Validation: To validate whether this procedure improves distinguishability of objects and background in histological images, we re-stain phantom images and N = 596 large histological images of immunostained samples of human solid tumors. We show that perceptual contrast is improved by a factor of 2.56 in phantom images and up to a factor of 2.17 in sets of histological tumor images.

Context: Thus, we provide an objective and reliable approach to measure object distinguishability in a given histological image and to maximize visual information available to a human observer. This method could easily be incorporated in digital pathology image viewing systems to improve accuracy and efficiency in research and diagnostics.

Show MeSH
Related in: MedlinePlus