Skin Optical Coherence Tomography Research Articles

Rapid measurement of epidermal thickness in OCT images of skin

Epidermal thickness (ET) changes are associated with several skin diseases. To measure ET, segmentation of optical coherence tomography (OCT) images is essential; manual segmentation is very time-consuming and requires training and some understanding of how to interpret OCT images. Fast results are important in order to analyze ET over different regions of skin in rapid succession to complete a clinical examination and enable the physician to discuss results with the patient in real time. The well-known CNN-graph search (CNN-GS) methodology delivers highly accurate results, but at a high computational cost. Our objective was to build a computational core, based on CNN-GS, able to accurately segment OCT skin images in real time. We accomplished this by fine-tuning the hyperparameters, testing a range of speed-up algorithms including pruning and quantization, designing a novel pixel-skipping process, and implementing the final product with efficient use of core and threads on a multicore central processing unit (CPU). We name this product CNN-GS-skin. The method identifies two defined boundaries on OCT skin images in order to measure ET. We applied CNN-GS-skin to OCT skin images, taken from various body sites of 63 healthy individuals. Compared with CNN-GS, our described method reduced computation time by 130 ×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes$$\\end{document} with minimal reduction in ET determination accuracy (from 96.38 to 94.67%).

Hydroquinone cream-based polymer microneedle roller for the combined treatment of large-area chloasma

Melasma is a common hyperpigmented skin condition that occurs on the face and other areas prone to light exposure, seriously affecting people's quality of life. Microneedle, a new type of transdermal drug delivery device, can significantly improve skin permeability. In this study, we designed and fabricated a polymer microneedle roller (PMR) using a mold hot pressing method, and established a mouse model of melasma induced by ultraviolet radiation. The dynamometer and insertion test of MNs into parafilm and skin of mice indicates that the MNs have sufficient mechanical properties to insert parafilm and skin of mice. The two methods (apply hydroquinone cream (HQC) directly and pre-treat with PMR before applying HQC) were used to treat melasma. From the results of skin surface observation, determination of superoxide dismutase (SOD) activity and malondialdehyde (MDA) content in skin and liver tissues, histological observation, and skin Optical coherence tomography (OCT), we confirmed both the two methods had a therapeutic effect while the PMR pretreatment group exhibited a better therapeutic effect. In addition, there were statistical differences between the UV group (P < 0.05). Together these results indicated that the MNs may be promising in future clinical applications in improving the UV irradiation-induced pigmentation like melisma.

Automatic Segmentation of Laser-Induced Injury OCT Images Based on a Deep Neural Network Model.

Optical coherence tomography (OCT) has considerable application potential in noninvasive diagnosis and disease monitoring. Skin diseases, such as basal cell carcinoma (BCC), are destructive; hence, quantitative segmentation of the skin is very important for early diagnosis and treatment. Deep neural networks have been widely used in the boundary recognition and segmentation of diseased areas in medical images. Research on OCT skin segmentation and laser-induced skin damage segmentation based on deep neural networks is still in its infancy. Here, a segmentation and quantitative analysis pipeline of laser skin injury and skin stratification based on a deep neural network model is proposed. Based on the stratification of mouse skins, a laser injury model of mouse skins induced by lasers was constructed, and the multilayer structure and injury areas were accurately segmented by using a deep neural network method. First, the intact area of mouse skin and the damaged areas of different laser radiation doses are collected by the OCT system, and then the labels are manually labeled by experienced histologists. A variety of deep neural network models are used to realize the segmentation of skin layers and damaged areas on the skin dataset. In particular, the U-Net model based on a dual attention mechanism is used to realize the segmentation of the laser-damage structure, and the results are compared and analyzed. The segmentation results showed that the Dice coefficient of the mouse dermis layer and injury area reached more than 0.90, and the Dice coefficient of the fat layer and muscle layer reached more than 0.80. In the evaluation results, the average surface distance (ASSD) and Hausdorff distance (HD) indicated that the segmentation results are excellent, with a high overlap rate with the manually labeled area and a short edge distance. The results of this study have important application value for the quantitative analysis of laser-induced skin injury and the exploration of laser biological effects and have potential application value for the early noninvasive detection of diseases and the monitoring of postoperative recovery in the future.

SKIN SURFACE CHEMISTRY AS A DIAGNOSTIC TOOL FOR SKIN DISEASES

The skin is the biggest sense organ in the body, with a surface area of 1.7m2 in adults. Because standard histological procedures influence skin components, several dermatological research has had little effectiveness in showing skin function. The structure of each skin layer may now be visualised non-invasively thanks to recent advances in non-invasive optical imaging. Individual skin components, on the other hand, remain difficult to identify. Understanding skin's chemical and physical features helps the cosmetics sector create deodorant, lipstick, and moisturizers. In addition, PH regulates the activation of proteases linked to the formation of chronic wounds and impacts skin barrier functions. Optical coherence tomography (OCT) is a non-invasive optical imaging innovation that creates high-resolution photos of the face and cross-areas of the skin. While OCT has a lot of potentials, many dermatologists are unfamiliar with it. This article aims to give professional dermatologists a basic grasp of skin OCT concepts and clinical applications.

Imaging depth adaptive resolution enhancement for optical coherence tomography via deep neural network with external attention

Optical coherence tomography (OCT) is a promising non-invasive imaging technique that owns many biomedical applications. In this paper, a deep neural network is proposed for enhancing the spatial resolution of OCT en face images. Different from the previous reports, the proposed can recover high-resolution en face images from low-resolution en face images at arbitrary imaging depth. This kind of imaging depth adaptive resolution enhancement is achieved through an external attention mechanism, which takes advantage of morphological similarity between the arbitrary-depth and full-depth en face images. Firstly, the deep feature maps are extracted by a feature extraction network from the arbitrary-depth and full-depth en face images. Secondly, the morphological similarity between the deep feature maps is extracted and utilized to emphasize the features strongly correlated to the vessel structures by using the external attention network. Finally, the SR image is recovered from the enhanced feature map through an up-sampling network. The proposed network is tested on a clinical skin OCT data set and an open-access retinal OCT dataset. The results show that the proposed external attention mechanism can suppress invalid features and enhance significant features in our tasks. For all tests, the proposed SR network outperformed the traditional image interpolation method, e.g. bi-cubic method, and the state-of-the-art image super-resolution networks, e.g. enhanced deep super-resolution network, residual channel attention network, and second-order attention network. The proposed method may increase the quantitative clinical assessment of micro-vascular diseases which is limited by OCT imaging device resolution.

Double-path parallel convolutional neural network for removing speckle noise in different types of OCT images

Speckle noises widely exist in optical coherence tomography (OCT) images. We propose an improved double-path parallel convolutional neural network (called DPNet) to reduce speckles. We increase the network width to replace the network depth to extract deeper information from the original OCT images. In addition, we use dilated convolution and residual learning to increase the learning ability of our DPNet. We use 100 pairs of human retinal OCT images as the training dataset. Then we test the DPNet model for denoising speckles on four different types of OCT images, mainly including human retinal OCT images, skin OCT images, colon crypt OCT images, and quail embryo OCT images. We compare the DPNet model with the adaptive complex diffusion method, the curvelet shrinkage method, the shearlet-based total variation method, and the OCTNet method. We qualitatively and quantitatively evaluate these methods in terms of image smoothness, structural information protection, and edge clarity. Our experimental results prove the performance of the DPNet model, and it allows us to batch and quickly process different types of poor-quality OCT images without any parameter fine-tuning under a time-constrained situation.

Deep learning differentiates between healthy and diabetic mouse ears from optical coherence tomography angiography images.

We trained a deep learning algorithm to use skin optical coherence tomography (OCT) angiograms to differentiate between healthy and type 2 diabetic mice. OCT angiograms were acquired with a custom‐built OCT system based on an akinetic swept laser at 1322 nm with a lateral resolution of ∼13 μm and using split‐spectrum amplitude decorrelation. Our data set consisted of 24 stitched angiograms of the full ear, with a size of approximately 8.2 × 8.2 mm, evenly distributed between healthy and diabetic mice. The deep learning classification algorithm uses the ResNet v2 convolutional neural network architecture and was trained on small patches extracted from the full ear angiograms. For individual patches, we obtained a cross‐validated accuracy of 0.925 and an area under the receiver operating characteristic curve (ROC AUC) of 0.974. Averaging over multiple patches extracted from each ear resulted in the correct classification of all 24 ears.

Applications and future directions for optical coherence tomography in dermatology*

Optical coherence tomography (OCT) is a noninvasive optical imaging method that can generate high-resolution en face and cross-sectional images of the skin in vivo to a maximum depth of 2mm. While OCT holds considerable potential for noninvasive diagnosis and disease monitoring, it is poorly understood by many dermatologists. Here we aim to equip the practising dermatologist with an understanding of the principles of skin OCT and the potential clinical indications. We begin with an introduction to the technology and discuss the different modalities of OCT including angiographic (dynamic) OCT, which can image cutaneous blood vessels at high resolution. Next we review clinical applications. OCT has been most extensively investigated in the diagnosis of keratinocyte carcinomas, particularly basal cell carcinoma. To date, OCT has not proven sufficiently accurate for the robust diagnosis of malignant melanoma; however, the evaluation of abnormal vasculature with angiographic OCT is an area of active investigation. OCT, and in particular angiographic OCT, also shows promise in monitoring the response to therapy of inflammatory dermatoses, such as psoriasis and connective tissues disease. We additionally discuss a potential role for artificial intelligence in improving the accuracy of interpretation of OCT imaging data.

Automatic Segmentation of Epidermis and Hair Follicles in Optical Coherence Tomography Images of Normal Skin by Convolutional Neural Networks.

Optical coherence tomography (OCT) is a well-established bedside imaging modality that allows analysis of skin structures in a non-invasive way. Automated OCT analysis of skin layers is of great relevance to study dermatological diseases. In this paper, an approach to detect the epidermal layer along with the follicular structures in healthy human OCT images is presented. To the best of the authors' knowledge, the approach presented in this paper is the only epidermis detection algorithm that segments the pilosebaceous unit, which is of importance in the progression of several skin disorders such as folliculitis, acne, lupus erythematosus, and basal cell carcinoma. The proposed approach is composed of two main stages. The first stage is a Convolutional Neural Network based on U-Net architecture. The second stage is a robust post-processing composed by a Savitzky-Golay filter and Fourier Domain Filtering to fully define the borders belonging to the hair follicles. After validation, an average Dice of 0.83 ± 0.06 and a thickness error of 10.25 μm is obtained on 270 human skin OCT images. Based on these results, the proposed method outperforms other state-of-the-art methods for epidermis segmentation. It demonstrates that the proposed image segmentation method successfully detects the epidermal region in a fully automatic way in addition to defining the follicular skin structures as main novelty.

P161 Optical coherence tomography of the skin detects scleroderma changes in clinically unaffected skin: an opportunity for early detection of systemic sclerosis

Abstract Background The Very Early Diagnosis Of Systemic Sclerosis (VEDOSS) study has shown that 82% of patients with Raynaud’s phenomenon, specific ANA positivity and scleroderma pattern at nailfold video capillaroscopy will fulfill classification criteria within 5 years. This is suggesting that there is a sub-clinical window of opportunity to diagnose systemic sclerosis (SSc) before clinical manifestations occur. In this scenario, a non-invasive tool to diagnose SSc in clinically unaffected skin might improve the early detection of disease in at risk-patients. Optical coherence tomography (OCT) of the skin has been shown to be a sensitive and accurate biomarker of skin fibrosis in SSc. Here we aimed to assess the ability of skin OCT to detect SSc in clinically unaffected skin from a multicentre cohort. Methods Dorsal forearm skin of SSc patients and matched-healthy controls (HC) was evaluated using VivoSight scanner (Michelson Diagnostics). Mean A-scans (mean OCT signal plotted against depth-in-tissue) were derived as previously described. Minimum Optical Density (MinOD), Maximum OD (MaxOD) and OD at 300 micron-depth (OD300) were calculated. Clinical involvement was assessed by an operator blinded to OCT findings using the mRSS. Receiver-operating characteristic (ROC) curve analysis was carried out for MinOD, MaxOD, and OD300 to evaluate their ability to discriminate between SSc and HC. Statistical analysis was performed using GraphPad Prism software V.7.0. Results One hundred seventy four OCT images were collected from 87 subjects [43 SSc (39 Female, mean age 49.7±9.1 years) and 44 gender/age-matched healthy controls (HC) (36 Female, mean age 50.2±8.3 years)] in two different SSc centres. All patients fulfilled classification criteria for SSc. OCT measures demonstrated discriminative ability in SSc skin detection with any clinical skin involvement (0-3 at site of analysis) with an AUC of 0.73 (MinOD, 95%CI 0.64-0.81), 0.77 (MaxOD, 95%CI 0.7-0.85) and 0.82 (OD300, 95%CI 0.76-0.89); p &amp;lt; 0.0001 for all as previously indicated. Most importantly, all three measures showed comparable performance in detecting scleroderma also in clinically unaffected skin (mRss=0 at site of analysis), with an AUC of 0.7 (95%CI 0.6-0.81, p = 0.001), 0.72 (95%CI 0.61-0.83, p = 0.0003) and 0.72 (95%CI 0.61-0.83, p = 0.0003) for MinOD, MaxOD and OD300 respectively. Conclusion Virtual biopsy by OCT recognises clinically unaffected skin of SSc patients from the HC skin. This is consistent with gene array data showing that scleroderma specific signatures are consistent in affected and clinically unaffected skin. These results inform future studies on at risk patients with clinically unaffected skin which may define a role for OCT in detecting subclinical SSc. Disclosures G. Abignano None. D. Temiz Karadag None. O. Gundogdu None. G. Lettieri None. M. Padula None. A.A. Padula None. P. Emery None. S. D'Angelo None. F. Del Galdo None.

Techniques and Applications in Skin OCT Analysis.

The skin is the largest organ of our body. Skin disease abnormalities which occur within the skin layers are difficult to examine visually and often require biopsies to make a confirmation on a suspected condition. Such invasive methods are not well-accepted by children and women due to the possibility of scarring. Optical coherence tomography (OCT) is a non-invasive technique enabling in vivo examination of sub-surface skin tissue without the need for excision of tissue. However, one of the challenges in OCT imaging is the interpretation and analysis of OCT images. In this review, we discuss the various methodologies in skin layer segmentation and how it could potentially improve the management of skin diseases. We also present a review of works which use advanced machine learning techniques to achieve layers segmentation and detection of skin diseases. Lastly, current challenges in analysis and applications are also discussed.

Optical coherence tomography in the assessment of acute changes in cutaneous vascular diameter induced by heat stress.

There are limited imaging technologies available that can accurately assess or provide surrogate markers of the in vivo cutaneous microvessel network in humans. In this study, we establish the use of optical coherence tomography (OCT) as a novel imaging technique to assess acute changes in cutaneous microvessel area density and diameter in humans. OCT speckle decorrelation images of the skin on the ventral side of the forearm up to a depth of 500 μm were obtained prior to and following 20-25 min of lower limb heating in eight healthy men [30.3 ± 7.6 (SD) yr]. Skin red blood cell flux was also collected using laser Doppler flowmetry probes immediately adjacent to the OCT skin sites, along with skin temperature. OCT speckle decorrelation images were obtained at both baseline and heating time points. Forearm skin flux increased significantly (0.20 ± 0.15 to 1.75 ± 0.38 cutaneous vascular conductance, P < 0.01), along with forearm skin temperature (32.0 ± 1.2 to 34.3 ± 1.0°C, P < 0.01). Quantitative differences in the automated calculation of vascular area densities (26 ± 9 to 49 ± 19%, P < 0.01) and individual microvessel diameters (68 ± 17 to 105 ± 25 μm, P < 0.01) were evident following the heating session. This is the first in vivo within-subject assessment of acute changes in the cutaneous microvasculature in response to heating in humans and highlights the use of OCT as an exciting new imaging approach for skin physiology and clinical research.

Cryosurgery Treatment of Actinic Keratoses Monitored by Optical Coherence Tomography: A Pilot Study

Background: Optical coherence tomography (OCT) is a non-invasive optical imaging technique providing high-resolution images. OCT may be useful as a monitoring tool during treatment of actinic keratoses (AK) and skin cancer. Objective: To examine and describe how OCT skin morphology changes when the tissue is exposed to the effects of cryotherapy. Methods: Normal ex vivo skin and in vivo AK lesions were examined. Cryotherapy was applied and OCT images were acquired at defined time points. OCT morphology was described. Results: Cryotherapy treatment produced an opaque iceball, and freezing depth could not be monitored by OCT. Vesicle formation after cryotherapy could be identified in OCT images. In ex vivo skin no vesicle formation occurred. Conclusion: OCT cannot monitor the freezing depth, but OCT was able to visualise AK lesions and vesicle formation shortly after cryotherapy. Results add to the assumption that OCT could be used in monitoring non-invasive treatments.

Speckle reduction in optical coherence tomography images of human finger skin by wavelet modified BM3D filter

Speckle pattern, which is inherent in coherence imaging, degrades the visual quality of optical coherence tomography (OCT) images. Speckle noise reduction is an important post-processing step for OCT imaging. As multiplicative noise pattern, speckle noise is difficult to suppress efficiently. Current well know speckle removal techniques are not able to achieve the goals both on speckle suppression and on edge details preservation. To address this issue, a novel speckle noise reduction algorithm is proposed. The algorithm is based on block-matching 3D filter modified by morlet wavelet decomposition. Original OCT image data transformed by logarithmic compression is decomposed into 10 components by morlet wavelet for three levels. Each component is proposed by a suited BM3D filter and the output image is reconstructed by wavelet reverse transformation. Experiment to in vivo human index finger skin OCT images showed that the proposed algorithm achieves good performances in terms of signal-to-noise ratio, equivalent number of looks, contrast-to-noise ratio, edge preservation coefficient, and CPU time compared to other recent high performance image speckle denoising algorithms. Visual comparisons also show that the proposed algorithm provides effective speckle noise suppression while preserving edge sharpness and improving morphological details visibility, such as different layers in finger skin OCT images.

OCT skin image enhancement through attenuation compensation

The enhancement of optical coherence tomography (OCT) skin images can help dermatologists investigate the morphologic information of the images more effectively. In this paper, we propose an enhancement algorithm with the stages that includes speckle reduction, skin layer detection, and attenuation compensation. A weighted median filter is designed to reduce the level of speckle while preserving the contrast. A novel skin layer detection technique is then applied to outline the main skin layers: stratum corneum, epidermis, and dermis. The skin layer detection algorithm does not make any assumption about the structure of the skin. A model of the light attenuation is then used to estimate the attenuation coefficient of the stratum corneum, epidermis, and dermis layers. The performance of the algorithm has been evaluated qualitatively based on visual evaluation and quantitatively using two no-reference quality metrics: signal-to-noise ratio and contrast-to-noise ratio. The enhancement algorithm is tested on 35 different skin OCT images, which show significant improvements in the quality of the images, especially in the structures at deeper levels.

Reduction of image artifacts in three-dimensional optical coherence tomography of skin in vivo

This paper presents results of in vivo studies on the effect of refractive index-matching media on image artifacts in optical coherence tomography (OCT) images of human skin. These artifacts present as streaks of artificially low backscatter and displacement or distortion of features. They are primarily caused by refraction and scattering of the OCT light beam at the skin surface. The impact of the application of glycerol and ultrasound gel is assessed on both novel skin-mimicking phantoms and in vivo human skin, including assessment of the epidermal thickening caused by the media. Based on our findings, recommendations are given for optimal OCT imaging of skin in vivo.

Role of multiple scattering in formation of OCT skin images

Optical coherence tomography (OCT) images of model human skin samples are obtained by using Monte Carlo simulations. The contributions of least and multiple scattering, diffusion and nondiffusion components and of separate scattering orders are studied by using a multilayer skin model based on experimental images. The model images are obtained by neglecting speckles or taking them into account. It is shown that least scattering forms the image of the upper skin layers, while the contribution of multiple scattering can be characterised as a blurred full image with a lower contrast. Repeated scattering mainly contributes to the OCT image at depths up to 1 mm. The diffusion component contributes to the image beginning from the epidermal basal layer. The partial image produced by this component is more blurred compared to the partial image produced by to multiple scattering. The nondiffusion component forms the OCT skin image at depths up to ≈1.3 mm.

Corrections to “Sonophoretic Delivery for Contrast and Depth Improvement in Skin Optical Coherence Tomography”

Tthe affiliation of the first author was published incorrectly. Additional corrections are made to the abstract and text of the original article.

Sonophoretic Delivery for Contrast and Depth Improvement in Skin Optical Coherence Tomography

Our previous studies demonstrated the feasibility of using a sonophoretic delivery method to enhance skin light transmittance with topical application of optical clearing agents using spectroscopy. In this study, we examined the effect of ultrasound [surgeon-performed (SP)] on optical coherence tomography (OCT) imaging depth and contrast of in vitro and in vivo skin. Sixty percent glycerol (G) and SP with a frequency of 1 MHz and a power of 0.75 W over a 3 cm probe was simultaneously applied for 15 min. We find that 60% G/SP results in a twofold increase in achievable OCT imaging depth for in vitro porcine skin and induces 11% shrinkage of the skin. For in vivo human skin, OCT imaging depth and contrast is significantly improved within 30 min of treatment. Imaging depth is increased from 1.4 to 2 mm, and dermal vasculature is clearly visualized in the deeper tissue. OCT imaging of the skin treated with 60% glycerol shows little enhancement in contrast or imaging depth over 60 min. We first demonstrate the superb ability of sonophoretic delivery for in vivo human skin optical clearing, particularly in accelerating the clearing rate. The greater clearing efficiency of glycerol implemented with ultrasound may be attributed to more effective dehydration.

Optical coherence tomography of skin for measurement of epidermal thickness by shapelet-based image analysis

Optical coherence tomography (OCT) provides a non-invasive method for in-vivo imaging of sub-surface skin tissue. Many skin features such as sweat glands and blisters are clearly observable in OCT images. It seems therefore probable that OCT could be used for the detection and identification of lesions and skin cancers. These applications, however, have not been well developed. One area in dermatology where OCT has been applied is the measurement of epidermal thickness. OCT images are inherently noisy and measurements based on them require intensive manual processing. A robust method to automatically detect and measure features of interest is necessary to enable routine application of OCT. As a first step, we approach the seemingly straightforward problem of measuring epidermal thickness. In this paper we describe a novel shapelet-based image processing technique for the automatic identification of the upper and lower boundaries of the epidermis in living human skin tissue. These boundaries are used to measure epidermal thickness. To our knowledge, this is the first report of automated feature identification and measurement from OCT images of skin.