A Value Mapping Virtual Staining Framework for Large-scale Histological Imaging

TL;DR

This paper introduces a versatile virtual staining framework using a novel value mapping loss and confidence-based tiling, enabling accurate large-scale histological image transformation with reduced artifacts.

Contribution

It proposes the VM-GAN with a value mapping loss and a confidence-based tiling method to improve large-scale virtual staining accuracy and consistency.

Findings

Achieves superior quantitative performance metrics.

Provides improved visual quality of virtual stained images.

Effectively reduces boundary artifacts in large images.

Abstract

The emergence of virtual staining technology provides a rapid and efficient alternative for researchers in tissue pathology. It enables the utilization of unlabeled microscopic samples to generate virtual replicas of chemically stained histological slices, or facilitate the transformation of one staining type into another. The remarkable performance of generative networks, such as CycleGAN, offers an unsupervised learning approach for virtual coloring, overcoming the limitations of high-quality paired data required in supervised learning. Nevertheless, large-scale color transformation necessitates processing large field-of-view images in patches, often resulting in significant boundary inconsistency and artifacts. Additionally, the transformation between different colorized modalities typically needs further efforts to modify loss functions and tune hyperparameters for independent training of networks. In this study, we introduce a general virtual staining framework that is adaptable to various conditions. We propose a loss function based on the value mapping constraint to ensure the accuracy of virtual coloring between different pathological modalities, termed the Value Mapping Generative Adversarial Network (VM-GAN). Meanwhile, we present a confidence-based tiling method to address the challenge of boundary inconsistency arising from patch-wise processing. Experimental results on diverse data with varying staining protocols demonstrate that our method achieves superior quantitative indicators and improved visual perception.