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Fig. 1 | PhotoniX

Fig. 1

From: Digital staining in optical microscopy using deep learning - a review

Fig. 1

Basic principle of Digital staining. a Conventional staining of 3D tissue samples requires a time-demanding and cumbersome procedure of biopsy acquisition, formalin-fixed paraffin-embedding (FFPE), manual sectioning, dehydration and artificial staining. These prepared tissue slices are then imaged by optical microscopes and the obtained images are quantified (e.g., via histopathology scoring by experienced experts). b Staining of cell cultures is conventionally based on antibody reactions with immuno-fluorescence (IF) stains. This process does not require embedding, sectioning and dehydration, and can even compatible with live cell imaging. However, the image quantification is still specific to the applied staining (e.g., nuclei staining for segmentation of nuclei). c Label-free optical technologies exploit the natural contrast of biomedical samples, without relying on artificial stainings. Although this omits the need for extensive sample preparation, the quantification is bound to the specific type of optical contrast that was used (e.g., dry mass approximation in quantitative phase imaging). d Digital staining (DS) can combine the advantages of an experimentally more practical imaging technique, with the high specificity of a thorough but cumbersome staining approach. Thus, DS can be used to digitally enhance label-free optical microscopy (e.g., generation of IF images based on white light microscopy) or to perform stain-to-stain translation (e.g., generation of specific IHC staining based on already available H&E stainings). A detailed literature overview of commonly used input-target image pairings and respective examples images is displayed in Fig. 2

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