Background: An accurate way to determine skin pigmentation is to acquire the spec-

Membrane lipid raft model has long been debated, but recently the concept of lipid submicrometric domains has emerged to characterize larger (micrometric) and more stable lipid membrane domains. Such domains organize signaling platforms involved in normal or pathological conditions. In this study, adhering human keratinocytes were investigated for their ability to organize such specialized lipid domains. Successful fluorescent probing of lipid domains, by either inserting exogenous sphingomyelin (BODIPY-SM) or using detoxified fragments of lysenin and theta toxins fused to mCherry, allowed specific, sensitive and quantitative detection of sphingomyelin and cholesterol and demonstrated for the first time submicrometric organization of lipid domains in living keratinocytes. Potential functionality of such domains was additionally assessed during replicative senescence, notably through gradual disappearance of SM-rich domains in senescent keratinocytes. Indeed, SM-rich domains were found critical to preserve keratinocyte migration before senescence, because sphingomyelin or cholesterol depletion in keratinocytes significantly alters lipid domains and reduce migration ability.

The segmentation of the dermal-epidermal junction (DEJ) in in vivo confocal images represents a challenging task due to uncertainty in visual labeling and complex dependencies between skin layers. We propose a method to segment the DEJ surface, which combines random forest classification with spatial regularization based on a three-dimensional conditional random field (CRF) to improve the classification robustness. The CRF regularization introduces spatial constraints consistent with skin anatomy and its biological behavior. We propose to specify the interaction potentials between pixels according to their depth and their relative position to each other to model skin biological properties. The proposed approach adds regularity to the classification by prohibiting inconsistent transitions between skin layers. As a result, it improves the sensitivity and specificity of the classification results.

Background Hyperspectral imaging for in vivo human skin study has shown great potential by providing non‐invasive measurement from which information usually invisible to the human eye can be revealed. In particular, maps of skin parameters including oxygen rate, blood volume fraction, and melanin concentration can be estimated from a hyperspectral image by using an optical model and an optimization algorithm. These applications, relying on hyperspectral images acquired with a high‐resolution camera especially dedicated to skin measurement, have yielded promising results. However, the data analysis process is relatively expensive in terms of computation cost, with calculation of full‐face skin property maps requiring up to 5 hours for 3‐megapixels hyperspectral images. Such a computation time prevents punctual previewing and quality assessment of the maps immediately after acquisition. Methods To address this issue, we have implemented a neural network that models the optimization‐based analysis algorithm. This neural network has been trained on a set of hyperspectral images, acquired from 204 patients and their corresponding skin parameter maps, which were calculated by optimization. Results The neural network is able to generate skin parameter maps that are visually very faithful to the reference maps much more quickly than the optimization‐based algorithm, with computation times as short as 2 seconds for a 3‐megapixel image representing a full face and 0.5 seconds for a 1‐megapixel image representing a smaller area of skin. The average deviation calculated on selected areas shows the network's promising generalization ability, even on wide‐field full‐face images. Conclusion Currently, the network is adequate for preview purposes, providing relatively accurate results in a few seconds.