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.

使用深度学习的数字染色技术 PhaseStain 能将实验室内不需要标记的组织切片的量化相位图像转变成相当于组织经过染色的亮场显微镜图像的等效图像，以 Hematoxylin 和 Eosin，Jones' stain 和 Masson's trichrome stain 技术染色的实验室内标本数字染色，能减少化学染色的需求，降低样品制备成本和节省时间。