Virtual staining for pathology: Challenges, limitations and perspectives

Abstract

In pathological examinations, tissue must first be stained to meet specific diagnostic requirements, a meticulous process demanding significant time and expertise from specialists. With advancements in deep learning, this staining process can now be achieved through computational methods known as virtual staining. This technique replicates the visual effects of traditional histological staining in pathological imaging, enhancing efficiency and reducing costs. Extensive research in virtual staining for pathology has already demonstrated its effectiveness in generating clinically relevant stained images across a variety of diagnostic scenarios. Unlike previous reviews that broadly cover the clinical applications of virtual staining, this paper focuses on the technical methodologies, encompassing current models, datasets, and evaluation methods. It highlights the unique challenges of virtual staining compared to traditional image translation, discusses limitations in existing work, and explores future perspectives. Adopting a macro perspective, we avoid overly intricate technical details to make the content accessible to clinical experts. Additionally, we provide a brief introduction to the purpose of virtual staining from a medical standpoint, which may inspire algorithm-focused researchers. This paper aims to promote a deeper understanding of interdisciplinary knowledge between algorithm developers and clinicians, fostering the integration of technical solutions and medical expertise in the development of virtual staining models. This collaboration seeks to create more efficient, generalized, and versatile virtual staining models for a wide range of clinical applications. • We present the first review of virtual staining from the perspectives of models and algorithms. • We present a comprehensive overview of challenges in virtual staining, limitations of current work, and future perspectives. • We discuss the distinctions between virtual staining and image translation, offering valuable insights for model developers.