Towards data-driven quantification of skin ageing using reflectance confocal microscopy.

Introduction: Evaluation of skin ageing is a non-standardized, subjective process, with typical measures relying coarse, qualitatively defined features. Reflectance confocal microscopy depth stacks contain indicators of both chrono-ageing and photo-ageing. We hypothesize that an ageing scale could be constructed using machine learning and image analysis, creating a data-driven quantification of skin ageing without human assessment.

Automated Segmentation of Skin Strata in Reflectance Confocal Microscopy Depth Stacks.

Reflectance confocal microscopy (RCM) is a powerful tool for in-vivo examination of a variety of skin diseases. However, current use of RCM depends on qualitative examination by a human expert to look for specific features in the different strata of the skin. Developing approaches to quantify features in RCM imagery requires an automated understanding of what anatomical strata is present in a given en-face section.

Automated detection of actinic keratoses in clinical photographs.

Background: Clinical diagnosis of actinic keratosis is known to have intra- and inter-observer variability, and there is currently no non-invasive and objective measure to diagnose these lesions.

Objective: The aim of this pilot study was to determine if automatically detecting and circumscribing actinic keratoses in clinical photographs is feasible.

Methods: Photographs of the face and dorsal forearms were acquired in 20 volunteers from two groups: the first with at least on actinic keratosis present on the face and each arm, the second with no actinic keratoses.

The future of keratinocyte skin cancer surveillance: automated image analysis to identify and monitor keratinocyte dysplasia.

Clinical assessment of actinic keratosis is known to be a variable process; however, there are currently no non-invasive alternatives for objectively assessing the condition besides excision and histopathology. While a number of technologies for examining potential actinic keratoses are under development, each of these still requires subjective human assessment. The existing approaches focus on assessing colour and texture features in clinical-scale images, such as those from dermoscopy and digital photography, and on structural or cellular characteristics in cellular-scale images, such as those from multiphoton microscopy and reflectance confocal microscopy.