Data from: 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. This work
presents an automated approach using a bag of features approach to
represent en-face sections and a logistic regression classifier to
classify sections into one of four classes (stratum corneum, viable
epidermis, dermal-epidermal junction and papillary dermis). This approach
was developed and tested using a dataset of 308 depth stacks from 54
volunteers in two age groups (20–30 and 50–70 years of age). The
classification accuracy on the test set was 85.6%. The mean absolute error
in determining the interface depth for each of the stratum corneum/viable
epidermis, viable epidermis/dermal-epidermal junction and dermal-epidermal
junction/papillary dermis interfaces were 3.1 μm, 6.0 μm and 5.5 μm
respectively. The probabilities predicted by the classifier in the test
set showed that the classifier learned an effective model of the anatomy
of human skin.