Virtualization of tissue staining in digital pathology using an unsupervised deep learning approach

TL;DR

This paper presents an unsupervised deep learning method to virtually stain tissue images in digital pathology, enabling multiplexing and reducing the need for physical staining procedures.

Contribution

It introduces a CycleGAN-based approach for virtual tissue staining and a novel method to mitigate tiling artifacts in the generated images.

Findings

Virtual staining closely matches real tissue images in analysis accuracy

The method reduces staining time and resource use

Effective artifact mitigation improves image quality

Abstract

Histopathological evaluation of tissue samples is a key practice in patient diagnosis and drug development, especially in oncology. Historically, Hematoxylin and Eosin (H&E) has been used by pathologists as a gold standard staining. However, in many cases, various target specific stains, including immunohistochemistry (IHC), are needed in order to highlight specific structures in the tissue. As tissue is scarce and staining procedures are tedious, it would be beneficial to generate images of stained tissue virtually. Virtual staining could also generate in-silico multiplexing of different stains on the same tissue segment. In this paper, we present a sample application that generates FAP-CK virtual IHC images from Ki67-CD8 real IHC images using an unsupervised deep learning approach based on CycleGAN. We also propose a method to deal with tiling artifacts caused by normalization layers and we validate our approach by comparing the results of tissue analysis algorithms for virtual and real images.