A 3d automatic segmentation method based on mathematical morphology for multiphoton images of melanocyte-keratinocyte coculture skin model

International audienceMelanocyte-keratinocyte coculture models are interesting in vitro systems used to identify the de-pigmenting or pro-pigmenting potential of cosmetic ingredients. This potential can be estimated by calculating the melanin density inside this model. Multiphoton microscopy is a privileged microscopy technique for this kind of evaluation, thanks to its low invasiveness and appropriate contrast (time resolved two photon excited fluorescence) for melanin detection. On multiphoton images, the first necessary step to calculate melanin density is to delimitate the pixels where the tissue is located. In this paper, we proposed a tissue segmentation method based on mathematical morphology.This pigmented coculture model contains two types of cells: keratinocytes and melanocytes that form a three dimensional tissue with a thickness of about 40 µm. The samples are reconstructed in 96 well plates, fixed in 4% formalin and rinsed in PBS prior to image acquisition. Multiphoton imaging was performed with a LEICA TCS SP8 microscopy at 760 nm, 40x/1.1NA W objective. A multiphoton 3D (x, y, z) image of 205x205x50 µm3 volume corresponds to a stack of 25 en face images of 512x512 pixels (0.4 µm/pixel) acquired with 2 µm z-step. In this kind of models, segmentation is made difficult by the fact that the intensity of the fluorescence signal is heterogeneous over the tissue: dark regions inside the images can correspond either to background or to cytoplasmic regions. Therefore, the first step of our method consists in applying a horizontal area closing with size A. This operator fills all 2D dark structures which are smaller than A, closing any possible small and dark connections between the exterior and the interior of the tissue. Parameter A is taken equal to 150 µm2, i.e. the area of a cell. Afterwards, a reconstruction by erosion, starting from the first and last slides, is applied in 3D. This operation fills all dark structures inside the tissue. Finally, a simple threshold at the noise level produces the final mask of the tissue. The method has been evaluated on a database containing 24 3D images, of which 4 had been manually segmented. The results were considered satisfactory by the experts. This tissue segmentation method has been integrated in a software suite and is robust and fast enough in order to be used in an automatic process. We are currently working on the following step, namely melanin quantification, to estimate the global melanin density, its z-distribution inside the tissue and localization in keratinocytes and melanocytes

Automatic 3D segmentation of multiphoton images: a key step for the quantification of human skin

International audienceBackground/purposeMultiphoton microscopy has emerged in the past decade as a useful noninvasive imaging technique for in vivo human skin characterization. However, it has not been used until now in evaluation clinical trials, mainly because of the lack of specific image processing tools that would allow the investigator to extract pertinent quantitative three-dimensional (3D) information from the different skin components.MethodsWe propose a 3D automatic segmentation method of multiphoton images which is a key step for epidermis and dermis quantification. This method, based on the morphological watershed and graph cuts algorithms, takes into account the real shape of the skin surface and of the dermal–epidermal junction, and allows separating in 3D the epidermis and the superficial dermis.ResultsThe automatic segmentation method and the associated quantitative measurements have been developed and validated on a clinical database designed for aging characterization. The segmentation achieves its goals for epidermis–dermis separation and allows quantitative measurements inside the different skin compartments with sufficient relevance.ConclusionsThis study shows that multiphoton microscopy associated with specific image processing tools provides access to new quantitative measurements on the various skin components. The proposed 3D automatic segmentation method will contribute to build a powerful tool for characterizing human skin condition. To our knowledge, this is the first 3D approach to the segmentation and quantification of these original images

APPLYING DEEP LEARNING TO MELANOCYTE COUNTING ON FLUORESCENT TRP1 LABELLED IMAGES OF IN VITRO SKIN MODEL

The original publication is available at www.ias-iss.orgInternational audienceCell counting is an important step in many biological experiments. It can be challenging, due to the large variability in contrast and shape of the cells, especially when their density is so high that the cells are closely packed together. Automation is needed to increase the speed and quality of the detection. In this study, a cell counting method is developed for images of melanocytes obtained after fluorescent labelling with TRP1 (Tyrosinase-related protein 1) of 3D reconstructed skin samples. Following previous approaches, a strategy based on predicting the local cell density, by means of a convolutional neural network (a U-Net), was adopted. The method showed great efficiency on a test set of 76 images, with an assessed counting error close to 10% on average, which is a commonly accepted target in cytology and histology. For comparison purposes, we have made our dataset publicly available

Multiphoton FLIM in cosmetic clinical research

International audienceThere is an increasing need in cosmetic clinical research for non-invasive, high content, skin imaging techniques offering the possibility on the one hand, to avoid performing invasive biopsies, and on the other hand, to supply a maximum of information on the skin state throughout a study, especially before, during and after product application. Multiphoton microscopy is one of these techniques compatible with in vivo human skin investigations, allowing human skin three-dimensional (3D) structure to be characterized with sub-µm resolution. In association with fluores-cence lifetime imaging (FLIM) and specific 3D-image processing, one can extract several quantitative parameters characterizing skin constituents in terms of morphology, density and organization. Various intracellular and extracellular constituents present specific endogenous signals enabling a non-invasive visualization of the 3D structure of epidermal and superficial dermal layers. Multiphoton FLIM applications in the cosmetic field range from knowledge to evaluation studies. Knowledge studies aim to acquire a better knowledge of skin differences appearing with aging, solar exposure or between the different skin phototypes. Evaluation studies deal with the efficacy of cosmetic anti-aging or whitening ingredients. The goal of this chapter is not to give a literature review of multiphoton FLIM applications in cosmetic clinical research, but rather to acquaint the reader with the quantitative 3D information afforded by multi-photon FLIM imaging of human skin and its interest in cosmetic clinical research

In vivo melanin 3D quantification and z-epidermal distribution by multiphoton FLIM, phasor and Pseudo-FLIM analyses

International audienceAbstract Characterizing melanins in situ and determining their 3D z-epidermal distribution is paramount for understanding physiological/pathological processes of melanin neosynthesis, transfer, degradation or modulation with external UV exposure or cosmetic/pharmaceutical products. Multiphoton fluorescence intensity- and lifetime-based approaches have been shown to afford melanin detection, but how can one quantify melanin in vivo in 3D from multiphoton fluorescence lifetime (FLIM) data, especially since FLIM imaging requires long image acquisition times not compatible with 3D imaging in a clinical setup? We propose an approach combining (i) multiphoton FLIM, (ii) fast image acquisition times, and (iii) a melanin detection method called Pseudo-FLIM, based on slope analysis of autofluorescence intensity decays from temporally binned data. We compare Pseudo-FLIM to FLIM bi-exponential and phasor analyses of synthetic melanin, melanocytes/keratinocytes coculture and in vivo human skin. Using parameters of global 3D epidermal melanin density and z-epidermal distribution profile, we provide first insights into the in vivo knowledge of 3D melanin modulations with constitutive pigmentation versus ethnicity, with seasonality over 1 year and with topical application of retinoic acid or retinol on human skin. Applications of Pseudo-FLIM based melanin detection encompass physiological, pathological, or environmental factors-induced pigmentation modulations up to whitening, anti-photoaging, or photoprotection products evaluation

In vivo multiphoton microscopy associated to 3D image processing for human skin characterization

International audienceMultiphoton microscopy has emerged in the past decade as a promising non-invasive skin imaging technique. The aim of this study was to assess whether multiphoton microscopy coupled to specific 3D image processing tools could provide new insights into the organization of different skin components and their age-related changes. For that purpose, we performed a clinical trial on 15 young and 15 aged human female volunteers on the ventral and dorsal side of the forearm using the DermaInspectR medical imaging device. We visualized the skin by taking advantage of intrinsic multiphoton signals from cells, elastic and collagen fibers. We also developed 3D image processing algorithms adapted to in vivo multiphoton images of human skin in order to extract quantitative parameters in each layer of the skin (epidermis and superficial dermis). The results show that in vivo multiphoton microscopy is able to evidence several skin alterations due to skin aging: morphological changes in the epidermis and modifications in the quantity and organization of the collagen and elastic fibers network. In conclusion, the association of multiphoton microscopy with specific image processing allows the three-dimensional organization of skin components to be visualized and quantified thus providing a powerful tool for cosmetic and dermatological investigations

Automatic segmentation of epidermal layers through in vivo skin multiphoton microscopy images

International audienc

Imagerie multiphoton de la peau humaine in vivo

National audienc

Dealing with Topological Information within a Fully Convolutional Neural Network

International audienceA fully convolutional neural network has a receptive field of limited size and therefore cannot exploit global information, such as topological information. A solution is proposed in this paper to solve this problem, based on pre-processing with a geodesic operator. It is applied to the segmentation of histological images of pigmented reconstructed epidermis acquired via Whole Slide Imaging