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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

Examples of digitally re-stained H&E images.(A-D) Normal lymph node tissue, (E-H) colorectal carcinoma tissue, (I-L) aspergillus, (M-P) breast cancer tissue surrounded by lymph node tissue. For each sample, the original image, the re-stained image and the original and resulting color map are shown. The color map used for re-staining was orange (#FFAD00)—blue (#006EFF). Sizes are: (A) 742 ∗ 742μm, (E) 742 ∗ 742μm, (N) 594 ∗ 594μm. (I) had no specified size (image source see List B in S1 File).
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pone.0145572.g008: Examples of digitally re-stained H&E images.(A-D) Normal lymph node tissue, (E-H) colorectal carcinoma tissue, (I-L) aspergillus, (M-P) breast cancer tissue surrounded by lymph node tissue. For each sample, the original image, the re-stained image and the original and resulting color map are shown. The color map used for re-staining was orange (#FFAD00)—blue (#006EFF). Sizes are: (A) 742 ∗ 742μm, (E) 742 ∗ 742μm, (N) 594 ∗ 594μm. (I) had no specified size (image source see List B in S1 File).

Mentions: The focus of our work was to enhance perceptual foreground-to-background contrast in H-DAB IHC images. However, the method we present can also be applied to other types of staining. As a proof of principle, we have arbitrarily chosen four sample H&E images and re-stained them using a orange—blue color map (Fig 8). Unlike in H-DAB IHC images, foreground and background are not clearly defined in these H&E images. Yet, it can be appreciated that some structures in the image are more clearly visible after re-staining (e.g. aspergillus in Fig 8I and 8J) and that virtual re-staining corrects discontinuities in the original color maps (e.g. Fig 8C, 8K and 8O). Also, virtual re-staining inherently corrects for illumination: While the original images in Fig 8A, 8E, 8I and 8M are not equally bright, these differences are removed after virtual re-staining (Fig 8B, 8F, 8J and 8N). Furthermore, we found that virtual re-staining can be successfully applied to Giemsa-stained images of bone marrow biopsies. In Fig 9, it can be seen that contrast between cytoplasm and cell nucleus is markedly increased by re-staining Giemsa stained images of human bone marrow.


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)

Examples of digitally re-stained H&E images.(A-D) Normal lymph node tissue, (E-H) colorectal carcinoma tissue, (I-L) aspergillus, (M-P) breast cancer tissue surrounded by lymph node tissue. For each sample, the original image, the re-stained image and the original and resulting color map are shown. The color map used for re-staining was orange (#FFAD00)—blue (#006EFF). Sizes are: (A) 742 ∗ 742μm, (E) 742 ∗ 742μm, (N) 594 ∗ 594μm. (I) had no specified size (image source see List B in S1 File).
© Copyright Policy
Related In: Results  -  Collection

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

pone.0145572.g008: Examples of digitally re-stained H&E images.(A-D) Normal lymph node tissue, (E-H) colorectal carcinoma tissue, (I-L) aspergillus, (M-P) breast cancer tissue surrounded by lymph node tissue. For each sample, the original image, the re-stained image and the original and resulting color map are shown. The color map used for re-staining was orange (#FFAD00)—blue (#006EFF). Sizes are: (A) 742 ∗ 742μm, (E) 742 ∗ 742μm, (N) 594 ∗ 594μm. (I) had no specified size (image source see List B in S1 File).
Mentions: The focus of our work was to enhance perceptual foreground-to-background contrast in H-DAB IHC images. However, the method we present can also be applied to other types of staining. As a proof of principle, we have arbitrarily chosen four sample H&E images and re-stained them using a orange—blue color map (Fig 8). Unlike in H-DAB IHC images, foreground and background are not clearly defined in these H&E images. Yet, it can be appreciated that some structures in the image are more clearly visible after re-staining (e.g. aspergillus in Fig 8I and 8J) and that virtual re-staining corrects discontinuities in the original color maps (e.g. Fig 8C, 8K and 8O). Also, virtual re-staining inherently corrects for illumination: While the original images in Fig 8A, 8E, 8I and 8M are not equally bright, these differences are removed after virtual re-staining (Fig 8B, 8F, 8J and 8N). Furthermore, we found that virtual re-staining can be successfully applied to Giemsa-stained images of bone marrow biopsies. In Fig 9, it can be seen that contrast between cytoplasm and cell nucleus is markedly increased by re-staining Giemsa stained images of human bone marrow.

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