  Deep learning-based virtual staining was developed to introduce image
contrast to label-free tissue sections, digitally matching the histological
staining, which is time-consuming, labor-intensive, and destructive to tissue.
Standard virtual staining requires high autofocusing precision during the whole
slide imaging of label-free tissue, which consumes a significant portion of the
total imaging time and can lead to tissue photodamage. Here, we introduce a
fast virtual staining framework that can stain defocused autofluorescence
images of unlabeled tissue, achieving equivalent performance to virtual
staining of in-focus label-free images, also saving significant imaging time by
lowering the microscope's autofocusing precision. This framework incorporates a
virtual-autofocusing neural network to digitally refocus the defocused images
and then transforms the refocused images into virtually stained images using a
successive network. These cascaded networks form a collaborative inference
scheme: the virtual staining model regularizes the virtual-autofocusing network
through a style loss during the training. To demonstrate the efficacy of this
framework, we trained and blindly tested these networks using human lung
tissue. Using 4x fewer focus points with 2x lower focusing precision, we
successfully transformed the coarsely-focused autofluorescence images into
high-quality virtually stained H&E images, matching the standard virtual
staining framework that used finely-focused autofluorescence input images.
Without sacrificing the staining quality, this framework decreases the total
image acquisition time needed for virtual staining of a label-free whole-slide
image (WSI) by ~32%, together with a ~89% decrease in the autofocusing time,
and has the potential to eliminate the laborious and costly histochemical
staining process in pathology.
