Biopsy-free in vivo virtual histology of skin using deep learning

Abstract An invasive biopsy followed by histological staining is the benchmark for pathological diagnosis of skin tumors. The process is cumbersome and time-consuming, often leading to unnecessary biopsies and scars. Emerging noninvasive optical technologies such as reflectance confocal microscopy (...

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Autores principales:	Jingxi Li, Jason Garfinkel, Xiaoran Zhang, Di Wu, Yijie Zhang, Kevin de Haan, Hongda Wang, Tairan Liu, Bijie Bai, Yair Rivenson, Gennady Rubinstein, Philip O. Scumpia, Aydogan Ozcan
Formato:	article
Lenguaje:	EN
Publicado:	Nature Publishing Group 2021
Materias:	Applied optics. Photonics TA1501-1820 Optics. Light QC350-467
Acceso en línea:	https://doaj.org/article/a6bbc41d9a374c1398572da75fb519bf
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spelling	oai:doaj.org-article:a6bbc41d9a374c1398572da75fb519bf2021-11-21T12:26:55ZBiopsy-free in vivo virtual histology of skin using deep learning10.1038/s41377-021-00674-82047-7538https://doaj.org/article/a6bbc41d9a374c1398572da75fb519bf2021-11-01T00:00:00Zhttps://doi.org/10.1038/s41377-021-00674-8https://doaj.org/toc/2047-7538Abstract An invasive biopsy followed by histological staining is the benchmark for pathological diagnosis of skin tumors. The process is cumbersome and time-consuming, often leading to unnecessary biopsies and scars. Emerging noninvasive optical technologies such as reflectance confocal microscopy (RCM) can provide label-free, cellular-level resolution, in vivo images of skin without performing a biopsy. Although RCM is a useful diagnostic tool, it requires specialized training because the acquired images are grayscale, lack nuclear features, and are difficult to correlate with tissue pathology. Here, we present a deep learning-based framework that uses a convolutional neural network to rapidly transform in vivo RCM images of unstained skin into virtually-stained hematoxylin and eosin-like images with microscopic resolution, enabling visualization of the epidermis, dermal-epidermal junction, and superficial dermis layers. The network was trained under an adversarial learning scheme, which takes ex vivo RCM images of excised unstained/label-free tissue as inputs and uses the microscopic images of the same tissue labeled with acetic acid nuclear contrast staining as the ground truth. We show that this trained neural network can be used to rapidly perform virtual histology of in vivo, label-free RCM images of normal skin structure, basal cell carcinoma, and melanocytic nevi with pigmented melanocytes, demonstrating similar histological features to traditional histology from the same excised tissue. This application of deep learning-based virtual staining to noninvasive imaging technologies may permit more rapid diagnoses of malignant skin neoplasms and reduce invasive skin biopsies.Jingxi LiJason GarfinkelXiaoran ZhangDi WuYijie ZhangKevin de HaanHongda WangTairan LiuBijie BaiYair RivensonGennady RubinsteinPhilip O. ScumpiaAydogan OzcanNature Publishing GrouparticleApplied optics. PhotonicsTA1501-1820Optics. LightQC350-467ENLight: Science & Applications, Vol 10, Iss 1, Pp 1-22 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Applied optics. Photonics TA1501-1820 Optics. Light QC350-467
spellingShingle	Applied optics. Photonics TA1501-1820 Optics. Light QC350-467 Jingxi Li Jason Garfinkel Xiaoran Zhang Di Wu Yijie Zhang Kevin de Haan Hongda Wang Tairan Liu Bijie Bai Yair Rivenson Gennady Rubinstein Philip O. Scumpia Aydogan Ozcan Biopsy-free in vivo virtual histology of skin using deep learning
description	Abstract An invasive biopsy followed by histological staining is the benchmark for pathological diagnosis of skin tumors. The process is cumbersome and time-consuming, often leading to unnecessary biopsies and scars. Emerging noninvasive optical technologies such as reflectance confocal microscopy (RCM) can provide label-free, cellular-level resolution, in vivo images of skin without performing a biopsy. Although RCM is a useful diagnostic tool, it requires specialized training because the acquired images are grayscale, lack nuclear features, and are difficult to correlate with tissue pathology. Here, we present a deep learning-based framework that uses a convolutional neural network to rapidly transform in vivo RCM images of unstained skin into virtually-stained hematoxylin and eosin-like images with microscopic resolution, enabling visualization of the epidermis, dermal-epidermal junction, and superficial dermis layers. The network was trained under an adversarial learning scheme, which takes ex vivo RCM images of excised unstained/label-free tissue as inputs and uses the microscopic images of the same tissue labeled with acetic acid nuclear contrast staining as the ground truth. We show that this trained neural network can be used to rapidly perform virtual histology of in vivo, label-free RCM images of normal skin structure, basal cell carcinoma, and melanocytic nevi with pigmented melanocytes, demonstrating similar histological features to traditional histology from the same excised tissue. This application of deep learning-based virtual staining to noninvasive imaging technologies may permit more rapid diagnoses of malignant skin neoplasms and reduce invasive skin biopsies.
format	article
author	Jingxi Li Jason Garfinkel Xiaoran Zhang Di Wu Yijie Zhang Kevin de Haan Hongda Wang Tairan Liu Bijie Bai Yair Rivenson Gennady Rubinstein Philip O. Scumpia Aydogan Ozcan
author_facet	Jingxi Li Jason Garfinkel Xiaoran Zhang Di Wu Yijie Zhang Kevin de Haan Hongda Wang Tairan Liu Bijie Bai Yair Rivenson Gennady Rubinstein Philip O. Scumpia Aydogan Ozcan
author_sort	Jingxi Li
title	Biopsy-free in vivo virtual histology of skin using deep learning
title_short	Biopsy-free in vivo virtual histology of skin using deep learning
title_full	Biopsy-free in vivo virtual histology of skin using deep learning
title_fullStr	Biopsy-free in vivo virtual histology of skin using deep learning
title_full_unstemmed	Biopsy-free in vivo virtual histology of skin using deep learning
title_sort	biopsy-free in vivo virtual histology of skin using deep learning
publisher	Nature Publishing Group
publishDate	2021
url	https://doaj.org/article/a6bbc41d9a374c1398572da75fb519bf
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_version_	1718419022699036672

Biopsy-free in vivo virtual histology of skin using deep learning

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