New Colors for Histology: Optimized Bivariate Color Maps Increase Perceptual Contrast in Histological Images
By Jakob Nikolas Kather, Cleo-Aron Weis, A. Marx, A. Schuster, Lothar Rudi Schad, Frank G. Zöllner. 2015
19 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Biophysics, Radiology, Nuclear Medicine and Imaging
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In Artificial Intelligence
- Jakob Nikolas Kather
- A. Marx
- Cleo-Aron Weis
- T. Gaiser
- Susanne Melchers
- Francesco Bianconi
- Lothar Rudi Schad
- Frank G. Zöllner
In Biophysics
In Radiology, Nuclear Medicine and Imaging
Citations in the corpus
- Multi-class texture analysis in colorectal cancer histology (2016, methodology): “In a previous study, we investigated the use of two-dimensional colour scales to visualize histological imaging data 37 .”