Virtual staining for histology by deep learning

Leena Latonen; Sonja Koivukoski; Umair Khan; Pekka Ruusuvuori

doi:10.1016/j.tibtech.2024.02.009

Virtual staining for histology by deep learning

Trends Biotechnol. 2024 Mar 13:S0167-7799(24)00038-6. doi: 10.1016/j.tibtech.2024.02.009. Online ahead of print.

Authors

Leena Latonen¹, Sonja Koivukoski², Umair Khan³, Pekka Ruusuvuori³

Affiliations

¹ Institute of Biomedicine, University of Eastern Finland, Kuopio, Finland. Electronic address: leena.latonen@uef.fi.
² Institute of Biomedicine, University of Eastern Finland, Kuopio, Finland.
³ Institute of Biomedicine, University of Turku, Turku, Finland.

PMID: 38480025
DOI: 10.1016/j.tibtech.2024.02.009

Abstract

In pathology and biomedical research, histology is the cornerstone method for tissue analysis. Currently, the histological workflow consumes plenty of chemicals, water, and time for staining procedures. Deep learning is now enabling digital replacement of parts of the histological staining procedure. In virtual staining, histological stains are created by training neural networks to produce stained images from an unstained tissue image, or through transferring information from one stain to another. These technical innovations provide more sustainable, rapid, and cost-effective alternatives to traditional histological pipelines, but their development is in an early phase and requires rigorous validation. In this review we cover the basic concepts of virtual staining for histology and provide future insights into the utilization of artificial intelligence (AI)-enabled virtual histology.

Keywords: artificial intelligence (AI); deep learning; histology; microscopy; pathology; virtual staining.

Publication types

Review