Dr. Georgios N. Stamatas Profile

Dr. Georgios N. Stamatas

Research Associate Director at Johnson & Johnson Consumer France SAS

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Publications (13)

SPIE Journal Paper | 2 August 2024 Open Access

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

Imane Lboukili, Georgios Stamatas, Xavier Descombes

JBO, Vol. 29, Issue 08, 086003, (August 2024) https://doi.org/10.1117/12.10.1117/1.JBO.29.8.086003

KEYWORDS: Education and training, Binary data, Tissues, Image segmentation, Confocal microscopy, Skin, Reflectivity, Tunable filters, Data modeling, Seaborgium

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SignificanceAccurate 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.AimWe aimed to design an automated pipeline for the analysis of the epidermal structure from RCM images.ApproachTwo attempts have been made at automatically localizing epidermal cells, called keratinocytes, on RCM images: the first is based on a rotationally symmetric error function mask, and the second on cell morphological features. Here, we propose a dual-task network to automatically identify keratinocytes on RCM images. Each task consists of a cycle generative adversarial network. The first task aims to translate real RCM images into binary images, thus learning the noise and texture model of RCM images, whereas the second task maps Gabor-filtered RCM images into binary images, learning the epidermal structure visible on RCM images. The combination of the two tasks allows one task to constrict the solution space of the other, thus improving overall results. We refine our cell identification by applying the pre-trained StarDist algorithm to detect star-convex shapes, thus closing any incomplete membranes and separating neighboring cells.ResultsThe results are evaluated both on simulated data and manually annotated real RCM data. Accuracy is measured using recall and precision metrics, which is summarized as the F1-score.ConclusionsWe demonstrate that the proposed fully unsupervised method successfully identifies keratinocytes on RCM images of the epidermis, with an accuracy on par with experts’ cell identification, is not constrained by limited available annotated data, and can be extended to images acquired using various imaging techniques without retraining.

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SPIE Journal Paper | 8 April 2023 Open Access

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

Imane Lboukili, Georgios Stamatas, Xavier Descombes

JBO, Vol. 28, Issue 04, 046003, (April 2023) https://doi.org/10.1117/12.10.1117/1.JBO.28.4.046003

KEYWORDS: Image segmentation, Skin, Education and training, Tunable filters, Machine learning, Digital filtering, Image filtering, Confocal microscopy, Visualization, Reflectivity

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SignificanceReflectance confocal microscopy (RCM) allows for real-time in vivo 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. Studying RCM images requires manual identification of cells to derive these properties, which is time consuming and subject to human error, highlighting the need for an automated cell identification method.AimWe aim to design an automated pipeline for the analysis of the structure of the epidermis from RCM images of the Stratum granulosum and Stratum spinosum.ApproachWe identified the region of interest containing the epidermal cells and the individual cells in the segmented tissue area using tubeness filters to highlight membranes. We used prior biological knowledge on cell size to process the resulting detected cells, removing cells that were too small and reapplying the used filters locally on detected regions that were too big to be considered a single cell. The proposed full image analysis pipeline (FIAP) was compared with machine learning-based approaches (cell cutter, different U-Net configurations, and loss functions).ResultsAll methods were evaluated both on simulated data (four images) and on manually annotated RCM data (seven images). Accuracy was measured using recall and precision metrics. Both accuracy metrics were higher in the proposed FIAP for both real (precision = 0.720 ± 0.068, recall = 0.850 ± 0.11) and synthetic images (precision = 0.835 ± 0.067, recall = 0.925 ± 0.012). The tested machine learning methods failed to identify and segment keratinocytes on RCM images with a satisfactory accuracy.ConclusionsWe showed that automatic cell segmentation can be achieved using a pipeline based on membrane detection, with an accuracy that matches expert manual cell identification. To our knowledge, this is the first method based on membrane detection to study healthy skin using RCM images evaluated against manually identified cell positions.

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SPIE Journal Paper | 25 July 2022 Open Access

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

Imane Lboukili, Georgios Stamatas, Xavier Descombes

JBO, Vol. 27, Issue 07, 070902, (July 2022) https://doi.org/10.1117/12.10.1117/1.JBO.27.7.070902

KEYWORDS: Skin, Reflectivity, Confocal microscopy, Image analysis, Biopsy, In vivo imaging, Melanoma, Machine learning, Image segmentation, Image classification

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Proceedings Article | 27 May 2022 Poster + Presentation + Paper

Automatic cell identification and analysis on in vivo reflectance confocal microscopy images of the human epidermis

Imane Lboukili, Xavier Descombes, Georgios Stamatas

Proceedings Volume 12144, 121440P (2022) https://doi.org/10.1117/12.2626777

KEYWORDS: Skin, Digital filtering, Image filtering, Confocal microscopy, In vivo imaging, Image segmentation

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SPIE Journal Paper | 5 March 2018 Open Access

Hequn Wang, Thomas Shyr, Michael Fevola, Gabriela Oana Cula, Georgios Stamatas

JBO, Vol. 23, Issue 03, 030501, (March 2018) https://doi.org/10.1117/12.10.1117/1.JBO.23.3.030501

KEYWORDS: Optical fibers, Collagen, Skin, Anisotropy, Second-harmonic generation, Multiphoton microscopy, In vivo imaging, Image analysis, Visualization, Algorithm development

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SPIE Journal Paper | 11 September 2015 Open Access

Geometrical and topological analysis of in vivo confocal microscopy images reveals dynamic maturation of epidermal structures during the first years of life

Jalil Bensaci, Zhao Yang Chen, M. Catherine Mack, Martial Guillaud, Georgios Stamatas

JBO, Vol. 20, Issue 09, 095004, (September 2015) https://doi.org/10.1117/12.10.1117/1.JBO.20.9.095004

KEYWORDS: Analytical research, Skin, Seaborgium, In vivo imaging, Confocal microscopy, Tissues, Image analysis, Statistical analysis, Cancer, Reflectivity

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Proceedings Article | 12 February 2010 Paper

The importance of optical methods for non-invasive measurements in the skin care industry

Georgios Stamatas

Proceedings Volume 7561, 75610X (2010) https://doi.org/10.1117/12.840938

KEYWORDS: Skin, Imaging spectroscopy, Tissue optics, In vivo imaging, Tissues, Confocal microscopy, Reflectivity, Luminescence, Fluorescence spectroscopy, Raman spectroscopy

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SPIE Journal Paper | 1 September 2007 Open Access

In vivo documentation of cutaneous inflammation using spectral imaging

Georgios Stamatas, Nikiforos Kollias

JBO, Vol. 12, Issue 05, 051603, (September 2007) https://doi.org/10.1117/12.10.1117/1.2798704

KEYWORDS: Skin, Inflammation, Imaging spectroscopy, Absorption, Chromophores, Tissues, In vivo imaging, Near infrared, Visualization, Visible radiation

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Proceedings Article | 13 July 2007 Paper

Objective evaluation of feature orientation in a two-dimensional image: applications on skin imaging

Georgios Stamatas

Proceedings Volume 6633, 66332B (2007) https://doi.org/10.1117/12.728372

KEYWORDS: Skin, Image filtering, Confocal microscopy, Gaussian filters, Algorithm development, In vivo imaging, Collagen, Signal to noise ratio, Convolution, Coherence (optics)

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Proceedings Article | 22 February 2006 Paper

Noninvasive quantitative documentation of cutaneous inflammation in vivo using spectral imaging

Georgios Stamatas, Nikiforos Kollias

Proceedings Volume 6078, 60780P (2006) https://doi.org/10.1117/12.647078

KEYWORDS: Skin, Imaging spectroscopy, Inflammation, Absorption, Visible radiation, Near infrared, Scattering, Chromophores, In vivo imaging, Tissues

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SPIE Journal Paper | 1 March 2004 Open Access

Blood stasis contributions to the perception of skin pigmentation

Georgios Stamatas, Nikiforos Kollias

JBO, Vol. 9, Issue 02, (March 2004) https://doi.org/10.1117/12.10.1117/1.1647545

KEYWORDS: Skin, Blood, Absorption, Chromophores, Diffuse reflectance spectroscopy, Visualization, Scattering, Ultraviolet radiation, Optical fibers, Tissues

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Proceedings Article | 2 July 2003 Paper

Hyperspectral image acquisition and analysis of skin

Georgios Stamatas, Costas Balas, Nikiforos Kollias

Proceedings Volume 4959, (2003) https://doi.org/10.1117/12.479491

KEYWORDS: Skin, Hyperspectral imaging, Chromophores, Absorption, Light scattering, Image acquisition, Image analysis, Blood, Image filtering, Algorithm development

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Proceedings Article | 1 May 2002 Paper

Visual versus spectroscopic analysis of skin color reactions: separation of contributing chromophores

Georgios Stamatas, Nikiforos Kollias

Proceedings Volume 4613, (2002) https://doi.org/10.1117/12.465258

KEYWORDS: Skin, Visualization, Spectroscopy, Chromophores, Diffuse reflectance spectroscopy, Absorption, Visual analytics, Optical fibers, Molecules, Visible radiation

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Showing 5 of 13 publications

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