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

Abstract

Reflectance confocal microscopy (RCM) allows real-time in vivo visualization of the skin at cellular level. The study of RCM images provides information on the topological and geometrical 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 timeconsuming and subject to human error, highlighting the need for an automated cell identification method. We propose an automated pipeline to analyze the structure of the skin in RCM images. The first step is to identify the region of interest (ROI) containing the epidermal cells. The second step is to identify individual cells in the segmented tissue area using an image filter. We then use prior biological knowledge to process the resulting detected cells, removing cells that are too small and reapplying the used filter locally on detected regions that are too big to be considered as a single cell. The results are evaluated both on simulated data and on manually annotated real RCM data. This study shows that automatic cell identification can be achieved, with an accuracy (precision and recall) that matches the interexpert variability.

Age‐dependent changes in epidermal architecture explored using an automated image analysis algorithm on in vivo reflectance confocal microscopy images

“…This work is in accordance with prior published work 27 and extends it by studying a broader age range across thousands of images. Using a novel approach for automated keratinocyte detection on RCM images 8 allowed us to quantitatively study epidermal cells spatial organization in different settings.…”

“…Subsequent analysis was focused only on images of the SG and SS, in which individual viable keratinocytes could be observed and were characterized by a grainy cytoplasm surrounded by bright, grainy membranes, forming a honeycomb pattern. 12,13 Further image analysis was conducted using a three-step approach 8 based on the intensity and morphological features (membrane thickness, length, and keratinocyte size) of keratinocytes visible in RCM images (Figure 1). The first step of the image analysis workflow was to identify and separate the regions of interest (ROI) of tissue containing keratinocytes from the dark background (microrelief lines).…”

“…We recently proposed a method for automating cell identification on RCM images 8 based on keratinocyte morphological features. The proposed approach is a three‐step pipeline to detect keratinocytes on RCM images of the Stratum Granulosum (SG) and S tratum Spinosum (SS).…”

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

“…This research was fully funded by Johnson & Johnson Santé Beauté France. Parts of this research were published in conference proceedings 42 …”

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

“…Unfortunately, image quality, heterogeneity, and low signal-to-noise ratio are a hurdle to automated methods’ development. Attempts at automating keratinocyte identification on RCM images have been made and were based on the identification of cell morphological features, e.g., membrane size, 9 , 10 but are hindered by manual parametrization often different among datasets, image types, and epidermal layers. Deep learning methods could be an alternative solution to circumvent these problems.…”