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Deep learning can eliminate skin biopsies by creating virtual histology of intact tissue
By: UCLA ITA
Recently, a team of researchers at UCLA used deep learning framework to transform RCM images of intact skin, obtained without a biopsy, into images that appear like biopsied, histochemically stained skin sections imaged on microscope slides. They trained a convolutional neural network using the generative adversarial scheme to rapidly transform in vivo RCM images of unstained skin into virtually-stained volumetric images of H&E. 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.
Published in Light: Science & Applications, a journal of the Springer Nature, this new 3D virtual staining framework can perform virtual histology on various skin conditions, including normal skin, basal cell carcinoma and melanocytic nevi with pigmented melanocytes, also covering different skin layers, including epidermis, dermal-epidermal junction and superficial dermis 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.
This research was led by Dr. Aydogan Ozcan, Chancellor's Professor and Volgenau Chair for Engineering Innovation at UCLA Electrical and Computer Engineering, in collaboration with Dr. Philip Scumpia, an Assistant Professor of Dermatology and Dermatolopathology at UCLA and the West Los Angeles VA Hospital, and Dr. Gennady Rubinstein, a dermatologist at the Dermatology & Laser Centre (Los Angeles). The other authors of this work include Jingxi Li, Jason Garfinkel, Xiaoran Zhang, Di Wu, Yijie Zhang, Kevin de Haan, Hongda Wang, Tairan Liu, Bijie Bai, and Adjunct Professor Dr. Yair Rivenson.
Original article: https://www.nature.com/