Characterization of color normalization methods in digital pathology whole slide images

Abstract

The color rendering of whole-slide images (WSIs) depends on factors involving the sample, such as tissue type, preparation methods, staining type and staining protocol, as well as equipment, such as the WSI scanner, WSI viewer, and WSI display. Variations in any of these steps may change the color rendering and therefore affect the performance of pathologists in the interpretation of WSIs and the robustness of artificial intelligence algorithms. In the literature, color normalization techniques have been proposed to reduce the color variations. The purpose of this work is to develop an objective approach to characterizing color normalization methods used in digital pathology. We employed color normalization methods to normalize the color rendered by a WSI scanner and then compared the normalized color with the actual scan by that scanner. The normalization errors were evaluated on the pixel level using the CIE color difference ΔE metric that have been shown to correlate with visually perceived differences in human vision. A selected set of 310 patch images of breast tissues scanned by two scanners from the ICPR 2014 MITOS & ATYPIA contest was used. Images from one scanner were color normalized to match the color rendering of the other scanner. Four color normalization methods were compared – Macenko, Reinhard, Vahadane, and StainGAN. Experimental results show that average color differences between two scanners in terms of ΔE were reduced from 16.2 before normalization to the range of [13.7,16.9] after normalization for the Macenko, Reinhard, Vahadane methods, and to 8.3 for the StainGAN method. Apparently the StainGAN method is significantly superior to the other three methods in terms of the ΔE metric. As such, we demonstrated a quantitative method for objectively evaluating color normalization techniques. Future work is needed to explore the relationship of the color fidelity measure and the impact of color normalization on pathologist and AI performance in clinical tasks.