PhaseStain: Digital staining of label-free quantitative phase microscopy images using deep learning
This work addresses the need for faster and cheaper tissue staining in pathology and biomedical research by eliminating chemical processes, though it is incremental as it applies existing deep learning methods to a new domain.
The authors tackled the problem of converting label-free quantitative phase microscopy images into histochemically stained brightfield images using a deep learning approach, achieving results that match chemically stained images for human skin, kidney, and liver tissue samples.
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.