Staining of tissues sections using chemical and biological dyes has been used for over a century for visualizing various tissue types and morphologic changes associated with contemporary cancer diagnosis. The staining procedure however is labor intensive, needs trained technicians, costly, and often results in loss of irreplaceable specimen and delays diagnoses. In collaboration with Brigham and Women's Hospital (Boston, MA), we describe a “computational staining” approach to digitally stain photographs of unstained tissue biopsies with Haematoxylin and Eosin (H&E) dyes to diagnose cancer.
Our method uses neural networks to rapidly stain photographs of non-stained tissues, providing physicians timely information about the anatomy and structure of the tissue. We also report a "computational destaining" algorithm that can remove dyes and stains from photographs of previously stained tissues, allowing reuse of patient samples.
These methods and neural networks assist physicians and patients by novel computational processes at the point-of-care, which can integrate seamlessly into clinical workflows in hospitals all over the world.