Stain normalization of histopathology images using generative adversarial networks

Abstract

Computational histopathology involves CAD for microscopic analysis of stained histopathological slides to study presence, localization or grading of disease. An important stage in a CAD system, stain color normalization, has been broadly studied. The existing approaches are mainly defined in the context of stain deconvolution and template matching. In this paper, we propose a novel approach to this problem by introducing a parametric, fully unsupervised generative model. Our model is based on end-to-end machine learning in the framework of generative adversarial networks. It can learn a nonlinear transformation of a set of latent variables, which are forced to have a prior Dirichlet distribution and control the color of staining hematoxylin and eosin (H&E) images. By replacing the latent variables of a source image with those extracted from a template image in the trained model, it can generate a new color copy of the source image while preserving the important tissue structures resembling the chromatic information of the template image. Our proposed method can instantly be applied to new unseen images, which is different from previous methods that need to compute some statistical properties on input test data. This is potentially problematic when the test sample sizes are limited. Experiments on H&E images from different laboratories show that the proposed model outperforms most state-of-the-art methods.