Epidermal three-dimensional (3D) topography/quantification has not been completely characterized yet. The recently developed line-field confocal optical coherence tomography (LC-OCT) provides real-time, high-resolution, in-vivo 3D imaging of the skin. This pilot study aimed at quantifying epidermal metrics (epidermal thicknesses, dermal-epidermal junction [DEJ] undulation and keratinocyte number/shape/size) using 3D LC-OCT. For each study participant (8 female, skintype-II, younger/older volunteers), seven body sites were imaged with LC-OCT. Epidermal metrics were calculated by segmentations and measurements assisted by artificial intelligence (AI) when appropriate. Thicknesses of epidermis/SC, DEJ undulation and keratinocyte nuclei volume varied across body sites. Evidence of keratinocyte maturation was observed in vivo: keratinocyte nuclei being small/spherical near the DEJ and flatter/elliptical near the skin surface. Skin microanatomy can be quantified by combining LC-OCT and AI.This technology could be highly relevant to understand aging processes and conditions linked to epidermal disorders. Future clinical/research applications are to be expected in this scenario.
Diagnosis based on histopathology for skin cancer detection is today’s gold standard and relies on the presence or absence of biomarkers and cellular atypia. However it suffers drawbacks: it requires a strong expertise and is time-consuming. Moreover the notion of atypia or dysplasia of the visible cells used for diagnosis is very subjective, with poor inter-rater agreement reported in the literature. Lastly, histology requires a biopsy which is an invasive procedure and only captures a small sample of the lesion, which is insufficient in the context of large fields of cancerization. Here we demonstrate that the notion of cellular atypia can be objectively defined and quantified with a non-invasive in-vivo approach in three dimensions (3D). A Deep Learning (DL) algorithm is trained to segment keratinocyte (KC) nuclei from Line-field Confocal Optical Coherence Tomography (LC-OCT) 3D images. Based on these segmentations, a series of quantitative, reproducible and biologically relevant metrics is derived to describe KC nuclei individually. We show that, using those metrics, simple and more complex definitions of atypia can be derived to discriminate between healthy and pathological skins, achieving Area Under the ROC Curve (AUC) scores superior than 0.965, largely outperforming medical experts on the same task with an AUC of 0.766. All together, our approach and findings open the door to a precise quantitative monitoring of skin lesions and treatments, offering a promising non-invasive tool for clinical studies to demonstrate the effects of a treatment and for clinicians to assess the severity of a lesion and follow the evolution of pre-cancerous lesions over time.
Line-field confocal optical coherence tomography (LC-OCT) is a non-invasive optical imaging technique based on a combination of the optical principles of optical coherence tomography and reflectance confocal microscopy with line-field illumination, which can generate cell-resolved images of the skin, in vivo, in vertical section, horizontal section and in three dimensions. This article reviews the optical principles of LC-OCT, including low coherence interferometry, confocal filtering and line-field arrangement. The optical setup allowing for the acquisition of color images of the skin surface in parallel with LC-OCT images, without compromising LC-OCT performance, is also presented. Practical use of LC-OCT is demonstrated through an overview of the workflow of examining a patient using a commercial handheld LC-OCT probe (deepLive™, DAMAE Medical), from creating the patient record in the software, acquiring the images, to reviewing and interpreting the images. LC-OCT can generate a significant amount of data, making automated deep learning algorithms particularly relevant for assisting in the analysis of LC-OCT images. A review of algorithms developed for skin layer segmentation, keratinocyte nuclei segmentation, and automatic detection of atypical keratinocyte nuclei is provided.
Line‐field confocal optical coherence tomography is a new imaging technique providing in vivo three‐dimensional images of the skin. Its spatial resolution allows standard measurements as SC/epidermis thicknesses or dermal‐epidermal junction undulation as well as innovative quantification of epidermis down to the cellular level based on artificial intelligence. Each keratinocyte can be counted and characterized by the size and shape of its nucleus. This technology produces epidermal metrics that can be useful both for research and current clinical practice.Further details can be found in the article by J. Chauvel‐Picard, V. Bérot, L. Tognetti, et al. (e202100236) image
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