An invasive biopsy followed by histological staining is the gold standard for traditional pathological identification of skin cancers, which requires a long processing time and often results in undesired biopsies and scarring. Reflectance confocal microscopy (RCM) can provide biopsy-free in vivo images of skin structure with a cellular-level resolution for skin disease evaluation; however, the RCM images are grayscale, miss nuclear features and have a low correlation with pathology, which requires specialized training for interpretation. Here, we report a deep learning-based method for performing non-invasive virtual skin histology using in vivo, label-free RCM images.
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Biopsy-free in vivo virtual histology of skin using deep learning
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.
more » « less- NSF-PAR ID:
- 10306760
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- Light: Science & Applications
- Volume:
- 10
- Issue:
- 1
- ISSN:
- 2047-7538
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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