Stain transformation using Mueller matrix guided generative adversarial networks

Abstract

Recently, virtual staining techniques have attracted more and more attention, which can help bypass the chemical staining process of traditional histopathological examination, saving time and resources. Meanwhile, as an emerging tool to characterize specific tissue structures in a label-free manner, the Mueller matrix microscopy can supplement more structural information that may not be apparent in bright-field images. In this Letter, we propose the Mueller matrix guided generative adversarial networks (MMG-GAN). By integrating polarization information provided by the Mueller matrix microscopy, the MMG-GAN enables the effective transformation of input H&E-stained images into corresponding Masson trichrome (MT)-stained images. The experimental results demonstrate the accuracy of the generated images by MMG-GAN and reveal the potential for more stain transformation tasks by incorporating the Mueller matrix polarization information, laying the foundation for future polarimetry-assisted digital pathology.