title: Empirical comparison of color normalization methods for epithelial-stromal classification in H and E images.
abstract: BACKGROUND
  Color normalization techniques for histology have not been empirically tested for their utility for computational pathology pipelines.
  
  
  OBJECTIVE
  We compared two contemporary techniques for achieving a common intermediate goal - epithelial-stromal classification.
  
  
  METHODS
  Expert-annotated regions of epithelium and stroma were treated as ground truth for comparing classifiers on original and color-normalized images.
  
  
  METHODS
  Epithelial and stromal regions were annotated on thirty diverse-appearing H and E stained prostate cancer tissue microarray cores. Corresponding sets of thirty images each were generated using the two color normalization techniques. Color metrics were compared for original and color-normalized images. Separate epithelial-stromal classifiers were trained and compared on test images. Main analyses were conducted using a multiresolution segmentation (MRS) approach; comparative analyses using two other classification approaches (convolutional neural network [CNN], Wndchrm) were also performed.
  
  
  METHODS
  For the main MRS method, which relied on classification of super-pixels, the number of variables used was reduced using backward elimination without compromising accuracy, and test - area under the curves (AUCs) were compared for original and normalized images. For CNN and Wndchrm, pixel classification test-AUCs were compared.
  
  
  RESULTS
  Khan method reduced color saturation while Vahadane reduced hue variance. Super-pixel-level test-AUC for MRS was 0.010-0.025 (95% confidence interval limits ± 0.004) higher for the two normalized image sets compared to the original in the 10-80 variable range. Improvement in pixel classification accuracy was also observed for CNN and Wndchrm for color-normalized images.
  
  
  CONCLUSIONS
  Color normalization can give a small incremental benefit when a super-pixel-based classification method is used with features that perform implicit color normalization while the gain is higher for patch-based classification methods for classifying epithelium versus stroma.
authors.affiliation: Department of Electronics and Electrical Engineering, Indian Institute of Technology Guwahati, Guwahati, Assam, India; Department of Pathology, University of Illinois, Chicago, IL, USA.
authors.forename: Amit
authors.initials: A
authors.lastname: Sethi
authors.affiliation: Department of Electrical and Computer Engineering, University of Illinois, Chicago, IL, USA.
authors.forename: Lingdao
authors.initials: L
authors.lastname: Sha
authors.affiliation: Department of Electronics and Electrical Engineering, Indian Institute of Technology Guwahati, Guwahati, Assam, India.
authors.forename: Abhishek Ramnath
authors.initials: AR
authors.lastname: Vahadane
authors.affiliation: Department of Pathology, University of Illinois, Chicago, IL, USA.
authors.forename: Ryan J
authors.initials: RJ
authors.lastname: Deaton
authors.affiliation: Department of Electronics and Electrical Engineering, Indian Institute of Technology Guwahati, Guwahati, Assam, India.
authors.forename: Neeraj
authors.initials: N
authors.lastname: Kumar
authors.affiliation: Department of Pathology, University of Illinois, Chicago, IL, USA.
authors.forename: Virgilia
authors.initials: V
authors.lastname: Macias
authors.affiliation: Department of Pathology, University of Illinois, Chicago, IL, USA.
authors.forename: Peter H
authors.initials: PH
authors.lastname: Gann
keywords: Color normalization; computational pathology; epithelial-stromal classification