Recently, a team of researchers using a deep learning framework developed RCM images to view intact skin without a biopsy.
However, the output of RCM images is not in a format that dermatologists and pathologists are familiar with, and analyzing these images requires specialized training since RCM images are in black and white, lack nuclear features, and reveal different planes within skin tissue compared to standard histology.
This technique, which the team calls “virtual histology”, allows analysis of microscopic images of the skin, bypasses several standard steps used for medical diagnosis, including skin biopsy, tissue fixation, processing, sectioning, as well as histochemical staining.
The research findings are published in the journal Light: Science & Applications.
This new 3D virtual staining framework can perform virtual histology on various skin conditions, including normal skin, and cover different skin layers.
The virtually-stained H&E images of unlabeled skin tissue showed similar color contrast and spatial features found in histochemically stained microscopic images of the biopsied tissue.
This deep learning-powered virtual histology approach can eliminate invasive skin biopsies and allow diagnosticians to see the overall histological features of intact skin, without the need for chemical processing or labeling of tissue.
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