Virtual histology of archaeological human deciduous prenatal enamel through synchrotron X-ray computed microtomography images

Virtual histology is increasingly utilized to reconstruct the cell mechanisms underlying dental morphology for fragile fossils when physical thin sections are not permitted. Yet, the comparability of data derived from virtual and physical thin sections is rarely tested. Here, the results from archaeological human deciduous incisor physical sections are compared with virtual ones obtained by phase-contrast synchrotron radiation computed microtomography (SRµCT) of intact specimens using a multi-scale approach. Moreover, virtual prenatal daily enamel secretion rates are compared with those calculated from physical thin sections of the same tooth class from the same archaeological skeletal series. Results showed overall good visibility of the enamel microstructures in the virtual sections which are comparable to that of physical ones. The highest spatial resolution SRµCT setting (effective pixel size = 0.9 µm) produced daily secretion rates that matched those calculated from physical sections. Rates obtained using the lowest spatial resolution setup (effective pixel size = 2.0 µm) were higher than those obtained from physical sections. The results demonstrate that virtual histology can be applied to the investigated samples to obtain reliable and quantitative measurements of prenatal daily enamel secretion rates.

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.