Colorimetrical Evaluation of Color Normalization Methods for HE-Stained Images

摘要

Color normalization is one of the pre-processing steps employed by many deep learning-based algorithms used for aiding pathology diagnoses with whole-slide images. Due to variability in tissue type, specimen preparation, staining protocol, and scanner performance, whole-slide images acquired from different sources may exhibit pronounced color variability that hinders algorithms from executing effectively. In the literature, numerous methods have been proposed to color-normalize hematoxylin and eosin (H&E)-stained images. However, the objective of color normalization has not been colorimetrically defined or evaluated beyond visual comparison. In this study, a quantitative metric, color normality, was defined to evaluate the degree of color similarity between images involved in a color normalization process. The pixel-wise spectral data of eight H&E-stained tissue slides were optically measured as the ground truth to test the Reinhard, Macenko, and Vahadane methods. Principal component analysis was conducted on the spectral data to derive a new color normalization method as the reference. Experiment results show that the H&E color gamut needs to be expressed with three components, but the widely used Macenko and Vahadane methods compressed the three-dimensional color gamut volume into a two-dimensional surface and reduced color gamut volumes by 40% or more. None of the color normalization methods could achieve a color normality of greater than 0.6174 when the image was not self-normalized.