Digital staining facilitates biomedical microscopy

Abstract

Traditional staining of biological specimens for microscopic imaging entails time-consuming, laborious, and costly procedures, in addition to producing inconsistent labeling and causing irreversible sample damage. In recent years, computational “virtual” staining using deep learning techniques has evolved into a robust and comprehensive application for streamlining the staining process without typical histochemical staining-related drawbacks. Such virtual staining techniques can also be combined with neural networks designed to correct various microscopy aberrations, such as out-of-focus or motion blur artifacts, and improve upon diffracted-limited resolution. Here, we highlight how such methods lead to a host of new opportunities that can significantly improve both sample preparation and imaging in biomedical microscopy.

Classifying breast cancer and fibroadenoma tissue biopsies from paraffined stain-free slides by fractal biomarkers in Fourier Ptychographic Microscopy

“…Among these several label-free techniques, Quantitative Phase Imaging (QPI) is recently emerging as a class of methods that can image label/stain-free biological samples while providing the necessary contrast for downstream analysis, physiology and histopathology observations. 22 QPI methods measure the optical path delay introduced by the biological sample on the light probe, which is linked to biophysical quantities. In QPI, the optical readout is the phase-contrast map.…”