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Histological staining is an indispensable tool for both biomedical research and clinical diagnosis of various diseases. However, current practices of histological staining often involve high-cost and laborious procedures with non-negligible environmental impact. Here we present a virtual histological staining framework that digitally stains unlabeled human tissue sections using autofluorescence microscopy and deep learning techniques. By training deep neural network models, we digitally replicated multiple stains on unlabeled tissue sections and accurately matched the images of the same samples after being histochemically stained in pathology labs. The success of our framework was demonstrated by predicting H&E stain, special stains, immunohistochemical stains, and immunofluorescence stains on multiple types of tissue sections, including lung, brain, breast, kidney, glands, etc. Beyond visual comparisons, blind studies led by board-certified pathologists further confirmed the equivalent staining quality and diagnostic values of the virtually stained images compared against their histochemically stained counterparts. By eliminating the variability introduced by the technician, reagents, environmental conditions, and digitization during histological staining, our virtual staining method produces consistent staining results across different sample slides, providing an ideal entry point for the ever-growing computer-aided pathological image analysis pipelines. More importantly, this non-destructive staining method allows the prediction of multiple stains on the same tissue section, promotes accurate evaluation of the exact same biological contents under multiple stains, which also helps reduce the sample volume that needs to be excised from the patients. To conclude, the presented virtual staining framework provides a label-free, high-quality, cost-effective, and eco-conscious method to the histological staining field.
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