DermoGAN: multi-task cycle generative adversarial networks for unsupervised automatic cell identification on reflectance confocal microscopy images of the human epidermis.

Significance: Accurate identification of epidermal cells on reflectance confocal microscopy (RCM) images is important in the study of epidermal architecture and topology of both healthy and diseased skin. However, analysis of these images is currently done manually and therefore time-consuming and subject to human error and inter-expert interpretation. It is also hindered by low image quality due to noise and heterogeneity.

Skin surface biomarkers are associated with future development of atopic dermatitis in children with family history of allergic disease.

Background: Atopic dermatitis (AD) is a common childhood chronic inflammatory skin disorder that can significantly impact quality of life and has been linked to the subsequent development of food allergy, asthma, and allergic rhinitis, an association known as the "atopic march."

Objective: The aim of this study was to identify biomarkers collected non-invasively from the skin surface in order to predict AD before diagnosis across a broad age range of children.

Methods: Non-invasive skin surface measures and biomarkers were collected from 160 children (3-48 months of age) of three groups: (A) healthy with no family history of allergic disease, (B) healthy with family history of allergic disease, and (C) diagnosed AD.

Skin maturation from birth to 10 years of age: Structure, function, composition and microbiome.

Infant and adult skin physiology differ in many ways; however, limited data exist for older children. To further investigate the maturation processes of healthy skin during childhood. Skin parameters were recorded in 80 participants of four age groups: babies (0-2 years), young children (3-6 years), older children (7-<10 years) and adults (25-40 years).

Age-dependent changes in epidermal architecture explored using an automated image analysis algorithm on in vivo reflectance confocal microscopy images.

Background: Reflectance confocal microscopy (RCM) allows for real-time in vivo visualization of the epidermis at the cellular level noninvasively. Parameters relating to tissue architecture can be extracted from RCM images, however, analysis of such images requires manual identification of cells to derive these parameters, which can be time-consuming and subject to human error, highlighting the need for an automated cell identification method.

Methods: First, the region-of-interest (ROI) containing cells needs to be identified, followed by the identification of individual cells within the ROI.

Automatic granular and spinous epidermal cell identification and analysis on reflectance confocal microscopy images using cell morphological features.

Significance: Reflectance confocal microscopy (RCM) allows for real-time visualization of the skin at the cellular level. The study of RCM images provides information on the structural properties of the epidermis. These may change in each layer of the epidermis, depending on the subject's age and the presence of certain dermatological conditions.

Automating reflectance confocal microscopy image analysis for dermatological research: a review.

Significance: Reflectance confocal microscopy (RCM) is a noninvasive, in vivo technology that offers near histopathological resolution at the cellular level. It is useful in the study of phenomena for which obtaining a biopsy is impractical or would cause unnecessary tissue damage and trauma to the patient.

Aim: This review covers the use of RCM in the study of skin and the use of machine learning to automate information extraction.