Three-dimensional conditional random field for the dermal-epidermal junction segmentation

Miniature line-scanned dual-axis confocal microscope for versatile clinical use

A miniature optical-sectioning fluorescence microscope with high sensitivity and resolution would enable non-invasive and real-time tissue inspection, with potential use cases including early disease detection and intraoperative guidance. Previously, we developed a miniature MEMS-based dual-axis confocal (DAC) microscope that enabled video-rate optically sectioned in vivo microscopy of human tissues. However, the device's clinical utility was limited due to a small field of view, a non-adjustable working distance, and a lack of a sterilization strategy. In our latest design, we have made improvements to achieve a 2x increase in the field of view (600 × 300 µm) and an adjustable working distance range of 150 µm over a wide range of excitation/emission wavelengths (488-750 nm), all while maintaining a high frame rate of 15 frames per second (fps). Furthermore, the device is designed to image through a disposable sterile plastic drape for convenient clinical use. We rigorously characterize the performance of the device and show example images of ex vivo tissues to demonstrate the optical performance of our new design, including fixed mouse skin and human prostate, as well as fresh mouse kidney, mouse intestine, and human head and neck surgical specimens with corresponding H&E histology. These improvements will facilitate clinical testing and translation.

Utilizing deep learning for dermal matrix quality assessment on in vivo line-field confocal optical coherence tomography images

BACKGROUND

Line-field confocal optical coherence tomography (LC-OCT) is an imaging technique providing non-invasive "optical biopsies" with an isotropic spatial resolution of ∼1  μm and deep penetration until the dermis. Analysis of obtained images is classically performed by experts, thus requiring long and fastidious training and giving operator-dependent results. In this study, the objective was to develop a new automated method to score the quality of the dermal matrix precisely, quickly, and directly from in vivo LC-OCT images. Once validated, this new automated method was applied to assess photo-aging-related changes in the quality of the dermal matrix.

MATERIALS AND METHODS

LC-OCT measurements were conducted on the face of 57 healthy Caucasian volunteers. The quality of the dermal matrix was scored by experts trained to evaluate the fibers' state according to four grades. In parallel, these images were used to develop the deep learning model by adapting a MobileNetv3-Small architecture. Once validated, this model was applied to the study of dermal matrix changes on a panel of 36 healthy Caucasian females, divided into three groups according to their age and photo-exposition.

RESULTS

The deep learning model was trained and tested on a set of 15 993 images. Calculated on the test data set, the accuracy score was 0.83. As expected, when applied to different volunteer groups, the model shows greater and deeper alteration of the dermal matrix for old and photoexposed subjects.

CONCLUSIONS

In conclusion, we have developed a new method that automatically scores the quality of the dermal matrix on in vivo LC-OCT images. This accurate model could be used for further investigations, both in the dermatological and cosmetic fields.

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

SIGNIFICANCE

Reflectance confocal microscopy (RCM) is a noninvasive, in vivo technology that offers near histopathological resolution at the cellular level. It is useful in the study of phenomena for which obtaining a biopsy is impractical or would cause unnecessary tissue damage and trauma to the patient.

AIM

This review covers the use of RCM in the study of skin and the use of machine learning to automate information extraction. It has two goals: (1) an overview of information provided by RCM on skin structure and how it changes over time in response to stimuli and in disease and (2) an overview of machine learning approaches developed to automate the extraction of key morphological features from RCM images.

APPROACH

A PubMed search was conducted with additional literature obtained from references lists.

RESULTS

The application of RCM as an in vivo tool in dermatological research and the biologically relevant information derived from it are presented. Algorithms for image classification to epidermal layers, delineation of the dermal-epidermal junction, classification of skin lesions, and demarcation of individual cells within an image, all important factors in the makeup of the skin barrier, were reviewed. Application of image analysis methods in RCM is hindered by low image quality due to noise and/or poor contrast. Use of supervised machine learning is limited by time-consuming manual labeling of RCM images.

CONCLUSIONS

RCM has great potential in the study of skin structures. The use of artificial intelligence could enable an easier, more reproducible, precise, and rigorous study of RCM images for the understanding of skin structures, skin barrier, and skin inflammation and lesions. Although several attempts have been made, further work is still needed to provide a definite gold standard and overcome issues related to image quality, limited labeled datasets, and lack of phenotype variability in available databases.

In vivo microscopy as an adjunctive tool to guide detection, diagnosis, and treatment

SIGNIFICANCE

There have been numerous academic and commercial efforts to develop high-resolution in vivo microscopes for a variety of clinical use cases, including early disease detection and surgical guidance. While many high-profile studies, commercialized products, and publications have resulted from these efforts, mainstream clinical adoption has been relatively slow other than for a few clinical applications (e.g., dermatology).

AIM

Here, our goals are threefold: (1) to introduce and motivate the need for in vivo microscopy (IVM) as an adjunctive tool for clinical detection, diagnosis, and treatment, (2) to discuss the key translational challenges facing the field, and (3) to propose best practices and recommendations to facilitate clinical adoption.

APPROACH

We will provide concrete examples from various clinical domains, such as dermatology, oral/gastrointestinal oncology, and neurosurgery, to reinforce our observations and recommendations.

RESULTS

While the incremental improvement and optimization of IVM technologies should and will continue to occur, future translational efforts would benefit from the following: (1) integrating clinical and industry partners upfront to define and maintain a compelling value proposition, (2) identifying multimodal/multiscale imaging workflows, which are necessary for success in most clinical scenarios, and (3) developing effective artificial intelligence tools for clinical decision support, tempered by a realization that complete adoption of such tools will be slow.

CONCLUSIONS

The convergence of imaging modalities, academic-industry-clinician partnerships, and new computational capabilities has the potential to catalyze rapid progress and adoption of IVM in the next few decades.

Line-field confocal optical coherence tomography as a tool for three-dimensional in vivo quantification of healthy epidermis: A pilot study

Epidermal three-dimensional (3D) topography/quantification has not been completely characterized yet. The recently developed line-field confocal optical coherence tomography (LC-OCT) provides real-time, high-resolution, in-vivo 3D imaging of the skin. This pilot study aimed at quantifying epidermal metrics (epidermal thicknesses, dermal-epidermal junction [DEJ] undulation and keratinocyte number/shape/size) using 3D LC-OCT. For each study participant (8 female, skin-type-II, younger/older volunteers), seven body sites were imaged with LC-OCT. Epidermal metrics were calculated by segmentations and measurements assisted by artificial intelligence (AI) when appropriate. Thicknesses of epidermis/SC, DEJ undulation and keratinocyte nuclei volume varied across body sites. Evidence of keratinocyte maturation was observed in vivo: keratinocyte nuclei being small/spherical near the DEJ and flatter/elliptical near the skin surface. Skin microanatomy can be quantified by combining LC-OCT and AI. This technology could be highly relevant to understand aging processes and conditions linked to epidermal disorders. Future clinical/research applications are to be expected in this scenario.

Towards data-driven quantification of skin ageing using reflectance confocal microscopy

INTRODUCTION

Evaluation of skin ageing is a non-standardized, subjective process, with typical measures relying coarse, qualitatively defined features. Reflectance confocal microscopy depth stacks contain indicators of both chrono-ageing and photo-ageing. We hypothesize that an ageing scale could be constructed using machine learning and image analysis, creating a data-driven quantification of skin ageing without human assessment.

METHODS

En-face sections of reflectance confocal microscopy depth stacks from the dorsal and volar forearm of 74 participants (36/18/20 training/testing/validation) were represented using a histogram of visual features learned using unsupervised clustering of small image patches. A logistic regression classifier was trained on these histograms to differentiate between stacks from 20- to 30-year-old and 50- to 70-year-old volunteers. The probabilistic output of the logistic regression was used as the fine-grained ageing score for that stack in the testing set ranging from 0 to 1. Evaluation was performed in two ways: on the test set, the AUC was collected for the binary classification problem as well as by statistical comparison of the scores for age and body site groups. Final validation was performed by assessing the accuracy of the ageing score measurement on 20 depth stacks not used for training or evaluating the classifier.

RESULTS

The classifier effectively differentiated stacks from age groups with a test set AUC of 0.908. Mean scores were significantly different when comparing age groups (mean 0.70 vs. 0.44; t = -6.62, p = 0.0000) and also when comparing stacks from dorsal and volar body sites (mean 0.64 vs. 0.53; t = 3.12, p = 0.0062). On the final validation set, 17 out of 20 depth stacks were correctly labelled.

DISCUSSION

Despite being limited to only coarse training information in the form of example stacks from two age groups, the trained classifier was still able to effectively discriminate between younger skin and older skin. Curiously, despite being only trained with chronological age, there was still evidence for measurable differences in age scores due to sun exposure-with marked differences in scores on sun-exposed dorsal sites of some volunteers compared with less sun-exposed volar sites. These results suggest that fine-grained data-driven quantification of skin ageing is achievable.

Skin strata delineation in reflectance confocal microscopy images using recurrent convolutional networks with attention

Reflectance confocal microscopy (RCM) is an effective non-invasive tool for cancer diagnosis. However, acquiring and reading RCM images requires extensive training and experience, and novice clinicians exhibit high discordance in diagnostic accuracy. Quantitative tools to standardize image acquisition could reduce both required training and diagnostic variability. To perform diagnostic analysis, clinicians collect a set of RCM mosaics (RCM images concatenated in a raster fashion to extend the field view) at 4-5 specific layers in skin, all localized in the junction between the epidermal and dermal layers (dermal-epidermal junction, DEJ), necessitating locating that junction before mosaic acquisition. In this study, we automate DEJ localization using deep recurrent convolutional neural networks to delineate skin strata in stacks of RCM images collected at consecutive depths. Success will guide to automated and quantitative mosaic acquisition thus reducing inter operator variability and bring standardization in imaging. Testing our model against an expert labeled dataset of 504 RCM stacks, we achieved [Formula: see text] classification accuracy and nine-fold reduction in the number of anatomically impossible errors compared to the previous state-of-the-art.

The Human Epidermal Basement Membrane: A Shaped and Cell Instructive Platform That Aging Slowly Alters

One of the most important functions of skin is to act as a protective barrier. To fulfill this role, the structural integrity of the skin depends on the dermal-epidermal junction—a complex network of extracellular matrix macromolecules that connect the outer epidermal layer to the underlying dermis. This junction provides both a structural support to keratinocytes and a specific niche that mediates signals influencing their behavior. It displays a distinctive microarchitecture characterized by an undulating pattern, strengthening dermal-epidermal connectivity and crosstalk. The optimal stiffness arising from the overall molecular organization, together with characteristic anchoring complexes, keeps the dermis and epidermis layers extremely well connected and capable of proper epidermal renewal and regeneration. Due to intrinsic and extrinsic factors, a large number of structural and biological changes accompany skin aging. These changes progressively weaken the dermal–epidermal junction substructure and affect its functions, contributing to the gradual decline in overall skin physiology. Most changes involve reduced turnover or altered enzymatic or non-enzymatic post-translational modifications, compromising the mechanical properties of matrix components and cells. This review combines recent and older data on organization of the dermal-epidermal junction, its mechanical properties and role in mechanotransduction, its involvement in regeneration, and its fate during the aging process.