GAN-based Virtual Re-Staining: A Promising Solution for Whole Slide Image Analysis

TL;DR

This paper introduces a conditional CycleGAN model for virtual IHC staining of histopathological images, enabling high-contrast tissue visualization without complex lab procedures, thus improving digital pathology analysis.

Contribution

The study develops an enhanced cCGAN with category conditions and structural loss functions, achieving more accurate multi-subdomain image translation for virtual staining.

Findings

Outperforms original CycleGAN in unpaired image translation tasks

Enables pixel-level segmentation with limited labeled data

Potentially applicable to various histology staining techniques

Abstract

Histopathological cancer diagnosis is based on visual examination of stained tissue slides. Hematoxylin and eosin (H\&E) is a standard stain routinely employed worldwide. It is easy to acquire and cost effective, but cells and tissue components show low-contrast with varying tones of dark blue and pink, which makes difficult visual assessments, digital image analysis, and quantifications. These limitations can be overcome by IHC staining of target proteins of the tissue slide. IHC provides a selective, high-contrast imaging of cells and tissue components, but their use is largely limited by a significantly more complex laboratory processing and high cost. We proposed a conditional CycleGAN (cCGAN) network to transform the H\&E stained images into IHC stained images, facilitating virtual IHC staining on the same slide. This data-driven method requires only a limited amount of labelled data but will generate pixel level segmentation results. The proposed cCGAN model improves the original network \cite{zhu_unpaired_2017} by adding category conditions and introducing two structural loss functions, which realize a multi-subdomain translation and improve the translation accuracy as well. % need to give reasons here. Experiments demonstrate that the proposed model outperforms the original method in unpaired image translation with multi-subdomains. We also explore the potential of unpaired images to image translation method applied on other histology images related tasks with different staining techniques.