PhaseStain: Digital staining of label-free quantitative phase microscopy images using deep learning

TL;DR

PhaseStain employs deep learning to digitally convert label-free quantitative phase images into stained microscopy images, potentially revolutionizing pathology by reducing costs and time associated with chemical staining.

Contribution

This work introduces a novel deep learning-based digital staining method, PhaseStain, for transforming quantitative phase images into virtually stained microscopy images.

Findings

Successfully generated stained images matching traditional histochemical stains.

Reduced need for chemical staining, saving time and costs.

Demonstrated applicability across multiple tissue types.

Abstract

Using a deep neural network, we demonstrate a digital staining technique, which we term PhaseStain, to transform quantitative phase images (QPI) of labelfree tissue sections into images that are equivalent to brightfield microscopy images of the same samples that are histochemically stained. Through pairs of image data (QPI and the corresponding brightfield images, acquired after staining) we train a generative adversarial network (GAN) and demonstrate the effectiveness of this virtual staining approach using sections of human skin, kidney and liver tissue, matching the brightfield microscopy images of the same samples stained with Hematoxylin and Eosin, Jones' stain, and Masson's trichrome stain, respectively. This digital staining framework might further strengthen various uses of labelfree QPI techniques in pathology applications and biomedical research in general, by eliminating the need for chemical staining, reducing sample preparation related costs and saving time. Our results provide a powerful example of some of the unique opportunities created by data driven image transformations enabled by deep learning.