This PDF file contains the front matter associated with SPIE Proceedings Volume 12352, including the Title Page, Copyright information, Table of Contents, and Conference Committee information.

We propose a method for digital hair removal from dermoscopic images that involves a new scheme for the acquisition of dermoscopic images to uncover hidden information by employing image fusion of multiple images of lesions. Classical approaches for the removal of hair from dermoscopic images are usually based on interpolation, pattern propagation or machine learning. These replace the hair pixels with calculated data aiming to reduce the impairment of the medical diagnostics. While these approaches are well established, the problem of information loss is not addressed. We show that our approach can lead to improved skin lesion assessment in dermoscopy.

Several optical technologies have been demonstrated as potential diagnostic tools for skin imaging, such as reflectance confocal microscopy (RCM) and optical coherence tomography (OCT). Although RCM could support cellular-level imaging, OCT could provide a larger field of view and a longer imaging depth than RCM. To quantitatively evaluate the skin condition with OCT, it is essential to develop an algorithm extracting different features, for example, the epidermis thickness and the optical characteristic of the epidermis. Although various segmentation algorithms have been proposed, most of the golden standards used involve the delineation of the boundary manually, where the labeling is highly relied on clinicians' experience and might vary among different physicians and the physician him or herself. Therefore, in this study, we collected skin OCT images of different sites from 20 subjects using a portable spectral-domain OCT system. The contrast of the OCT images can be effectively improved by optical attenuation coefficient (OAC) computation. This enables the development of a fully automatic segmentation algorithm, providing the parameters such as the (i) epidermis thickness and the (ii, iii) roughness of the boundary between the epidermis and air as well as the epidermis and dermis. Also, the (iv, v) OAC coefficients of the epidermis and upper dermis layer are available. Collectively, the developed algorithm supports the quantitative analysis of the five parameters across the imaging site with volumetric OCT imaging. We believe the developed algorithm can facilitate the implementation of skin OCT imaging for aesthetic medicine as a modality for objective pre- and post-treatment evaluation.

Artificial Intelligence (AI) has found its place within the field of medical diagnostics. It can assist in feature recognition, in image segmentation and enhancement or in assessing specific structures. In the field of dermatology, there is a special interest in AI supported tools to classify skin lesions.

However, in order to train such a classifier, lots of image data is needed. The available image data on skin lesions shows a large variety in size, resolution and lighting as there is no underlying acquisition standard.

These properties make up the main components of the image data but do not contain any information on the actual subject and hence do hinder the training process. In this work, a method for cleaning image data is presented. It aims to standardize the image data in the aspects of image size, section and lighting without lowering the resolution and while maintaining the relevant structures shown.

To achieve this, the images are framed to ensure they all have the same size. In the next step, microscopic images are selected and their region of interest is determined. The lighting is adjusted by standardizing the colors. Afterwards, the final size adjustment follows.

The results were evaluated by carrying out a t-distributed stochastic neighbor embedding as well as a Principal Component Analysis. It could be shown that the presented routine improves the separability of benign and malign skin lesions which can also speed up and increase the quality of training process of AI models.

Quantifying skin aging changes and characterizing its 3D structure and function in a non-invasive way is still a challenging area of research. In vivo multiphoton imaging offers means to assess skin constituents in 3D, however prior aging studies mostly focused on 2D analyses of dermal fibers through their signals’ intensities or densities.

We designed and implemented multiphoton multiparametric 3D quantification tools for in vivo human skin characterization. Despite the limited field of view of the technic, investigation of 2 regions of interest (ROIs) per zone per volunteer were found to be a good compromise in assessing 3D skin constituents in both epidermis and superficial dermis.

Skin aging was characterized on different UV exposed areas - ventral and dorsal forearms, face. Epidermal morphological changes occur late and were only objectified between extreme age groups. Melanin accumulated with age and chronic exposure on both forearms and appears earlier. Superficial dermal changes were mainly elastin density increase, also structurally modified from thin and straight fibers in young to dense and compact pattern in elder, and no obvious change in collagen density, both reflected by SHGto2PEF ratio and SAAID index decrease and ImbrN index increase. The second 20 μm-thickness normalized dermal sub-layer exhibited the highest aging differences. The 3D ImbrN index refines the share of photoaging in global aging on face and the 3D SAAID index on forearm.

Multiphoton multiparametric 3D skin quantification offers rich spatial information of interest in assessing normal human skin condition and its pathological, external environment or product induced changes.

Heating has been known to cure cancer for over 2000 years¹, and recent studies have confirmed this in the treatment of basal cell carcinomas (BCC)². The application of uncontrolled heating often results in unacceptable scarring. Our ability to control the delivery of heat using a forward-looking infrared (FLIR) camera and a modulated long pulsed (LP) Nd:YAG 1064nm laser has resulted in better outcomes in comparison to traditional destruction techniques³. Use of optical coherence tomography (OCT) enhances our ability to define the tumor margins prior to treatment. The combination of FLIR and OCT allows very precise treatment of BCCs with at least comparable outcomes to conventional LP Nd:YAG treatments.

Melanoma is the most aggressive type of skin cancer with the highest mortality rate, with surgery being the standard treatment. In this study, the effect of indocyanine green nanoemulsion in an animal model was evaluated for the treatment of cutaneous melanoma, using photothermal therapy. Different irradiation protocols, and nanoICG intratumoral and systemic delivery ways were tested. Macroscopic and histological analyses and Kaplan-Meier curves for animal survival are presented for comparison of the different investigated protocols.

Inadequate tissue perfusion is a fundamental cause of early complications following a range of procedures including the creations of skin flaps/grafts during reconstructive microsurgery and complex closures during amputation. Clinical examination remains the primary means of evaluating tissue perfusion intraoperatively. Recently, indocyanine green (ICG) angiography has been used as an adjunt to physical examination. However, ICG angiography is an invasive procedure that requires the intravenous application of a fluorescent dye.

Enhanced thermal imaging (ETI) is a non-invasive, real-time infrared imaging technique that can detect blood vessels embedded in soft tissue. ETI uses selective heating of blood via illumination with a green (532 nm) LED to produce a thermal contrast (≥ 0.5 ◦C) between blood vessels and surrounding water-rich tissue. Vessel-rich regions appear brighter in the thermal image. ETI does not require the use of dyes and recent improvements to the acquisition software have enabled real-time imaging. The compact footprint of the system could allow for use both intraoperatively and at the bedside.

In this study we evaluate the ability of ETI to assess tissue perfusion of skin flaps in a murine model. The healing and perfusion of these flaps was monitored via the density of capillary beds and vascular networks using visual inspection, fluorescent imaging, and ETI over a 12-day study period. We compare the ability of these techniques to detect early indications of necrosis and re-vascularization in grafts.

Wound healing assessment is usually performed visually by a trained physician. This type of evaluation is very subjective and returns limited information about the wound progression. In contrast, optical imaging techniques are non-invasive ways to quantitatively measure biological parameters. Spatial frequency domain imaging (SFDI) is an optical technique that exploits sinusoidal patterns of light with multiple spatial frequencies to measure the tissue frequency-specific response, from which the absorption and scattering coefficient of the material can be derived. While SFDI is based on models of light transport that assume the tissue is homogeneous, skin is composed by several layer with very different optical properties. An underutilized property of SFDI, however, is that the spatial frequency of the patterns determines the penetration depth of photons in the tissue. By using multiple ranges of spatial frequencies, we are developing a means to obtain morphological data from different volumes of tissue. This data is used to reconstruct the optical properties in depth, allowing us to differentiate between different thin layers of tissue. In this study we have developed a 2-layer optical phantom model with realistic optical properties and dimensions, that mimics the physiology of wound healing. We have used this physical model to validate the accuracy of this approach in obtaining layer specific optical properties.

Vital signs reflect circulatory function and hence hemodynamics on a macroscopic scale and are often unreliable or late indicators of hemodynamic instability. Previous studies support that alterations in the microcirculation may provide early indicators of deterioration and impending shock. Microcirculation is also restored late in the recovery process. Hence, monitoring microcirculation is important since treatments based on normalizing classical vital signs will not always restore microvascular hemodynamics and the microcirculation may remain in shock although e.g., blood pressure seems normal.

The aim of this study was to investigate alterations in skin microcirculation dynamics during lower body negative pressure as a model of shock and central hypovolemia. By using spatial frequency domain imaging (SFDI) and polarized reflectance imaging, we investigated the association between micro- and macrovascular function during these conditions. Furthermore, we evaluated microvascular reactivity using the capillary refill test.

A cohort of 9 subjects were subjected to a progressive lower body negative pressure (LBNP) protocol. At baseline and at LBNP = -20mmHg, -30mmHg and -40mmHg, SFDI images were acquired and analyzed for tissue hemoglobin content and oxygenation. Superficial hemoglobin content was estimated by polarized reflectance imaging. We found that microcirculatory reactivity was prolonged during LBNP, but recovered after end of the protocol. These results indicate a correlation between negative pressure and microcirculatory function and that may provide a basis for early detection of shock in emergency care settings.

Atopic dermatitis is a chronic, relapsing inflammatory skin disease, which has one of the highest prevalence rates in the world. It is widely believed to be driven by deficiencies in the skin barrier layer, which is provided by the stratum corneum. The stratum corneum thickness determines the absorption rate of topical treatments and may correlate with its barrier function of preventing allergen penetration. Here we demonstrate a free-space, portable Fourier domain visible-light optical coherence tomography (OCT) system to study the stratum corneum thickness (5-15 μm typically) in non-palmar human skin. We use a supercontinuum laser source, optimized for emission in the 500-700 nm range and achieve axial resolution of ~ 1 μm in tissue (for the refractive index n = 1.55 of the stratum corneum). Our system has ~ -85 dB sensitivity at an A-scan rate of 125 kHz and about 1 mW of optical power onto the skin. High-quality B-scans from various human skin sites are obtained, with a clearly resolved stratum corneum layer presenting as a hypoechogenic dark layer, similar to that found in palmar skin with traditional OCT systems.

We present in vivo optical properties of skin tissue assessed using spatially resolved diffuse reflectance spectroscopy. Data was collected in a sub-study of the Swedish CArdioPulmonary bioImage Study (SCAPIS). Measurements were performed on 3,809 subjects, 50-65 years, on the volar side of the forearm with the PeriFlux 6000 EPOS system.

The analysis consisted of an inverse Monte Carlo method where modeled spectra were non-linearly fitted to measured diffuse reflectance spectra at 0.4 and 1.2-mm source-detector fiber separations, respectively, between 475 and 850 nm. The model consisted of one epidermis layer with adaptable thickness and absorption caused by melanin, and two dermis layers. The upper dermis had a fixed thickness of 0.2 mm and the lower an infinite thickness. The absorption in the dermis layers depended on varying amounts of hemoglobin and its oxygen saturation. The reduced scattering coefficient, with three adaptable parameters, was equal in all layers in the model.

Absorption coefficient in epidermis times epidermis thickness, reflecting the total amount of melanin, was 0.19±0.16 [-] at 570 nm with a significant difference between winter (0.12±0.10) and summer (0.26±0.19). Melanin absorption implies an average of 3.9% tissue fraction of melanosomes in epidermis. Absorption coefficient at the 570 nm isosbestic point was 0.19±0.11 mm^-1 in the upper dermis layer and 0.10±0.05 mm^-1 in the lower. The reduced scattering coefficient was 2.3±0.5 mm-1 at 570 nm. Average sampling depth for all wavelengths and both separations was 0.43±0.03 mm.

In Asians, melanoma appears as pigmented lesions on the hands and feet, and is often diagnosed as acral malignant melanoma (ALM) in the late stage with a very poor prognosis. Among diverse clinical characteristics of melanoma, the presence of basement membrane involvement is one of the most important prognostic factors. However, there have been few studies reporting artificial intelligence for prediction of basement membrane involvement in ALMs beyond its diagnosis. Therefore, in this study, we present a deep learning model that predicts the basement membrane involvement of ALMs from dermoscopy images.

Laser skin treatment using micro-lens arrays (MLA) and diffractive optical elements (DOE) have clinically been applied to treat pigmented lesions [1,2]. However, DOE hardly has a deep penetration depth and MLA shows a non-uniform distribution of lesions, making it difficult to target lesions deeply located in the dermis [2]. The purpose of this study is to investigate the feasibility of diffractive lens arrays (DLA, combination of MLA and DOE) to treat the pigmented skin via laser-induced thermal decomposition at various tissue depth. Porcine skin tissue and one farm pig were used to compare spatial distributions of the laser-induced damage after irradiation with DOE, MLA, and DLA. 1064 nm picosecond Nd:YAG laser light was irradiated on a target tissue at 3.0 ~ 6.0 J/cm² under DOE, MLA, and DLA conditions. According to ex vivo tests, DOE generated laser-induced vacuoles near the basal membrane (208 ± 80 μm), and MLA distributed vacuoles more deeply located in dermis than DOE (382 ± 147 μm). On the other hand, DLA created laser-induced thermal decomposition deeply positioned in dermis (548 ± 137 μm). In vivo tests demonstrated that DOE generated the vacuolization in epidermis and dermis (235 ± 82 μm). On the other hand, DLA generated the deep laser-induced thermal decomposition in dermis (1057 ± 286 μm). The current study demonstrated that the picosecond laser irradiation with DLA could create the thermal decomposition in the deep dermis. Therefore, the DLA-assisted laser application may help to achieve selective skin treatment for deeply located pigments.

Traditional methods of wound diagnosis have been diagnosed and prescribed by the naked eye of an expert. If the wound segmentation algorithm is applied to the wound diagnosis, the area of wound can be quantitated and used as an auxiliary means of treatment. Even with dramatic development of Deep learning technology in recent years, However, a lack of datasets generally occurs overfitting problem of deep learning model, which leads to poor performance for external datasets. Therefore, we trained the wound segmentation model by adding a new wound dataset in addition to the existing Open dataset, the Diabetic Foot Ulcer Challenge Dataset. Machine learning based methods are used when producing new dataset, ground truth images. Thus, in addition to the manual methods, Gradient Vector Flow machine learning techniques is used for ground-truth image production to reduce the time consumed in vain. The wound segmentation model used in this study is a U-net with residual block combined with cross entropy loss and Dice loss. As a result of the experiment, the wound segmentation accuracy was about 90% for Dice coefficient

Optical coherence tomography (OCT) is well-known for its high-resolution, non-invasive imaging modality with many medical uses, including skin imaging. Nevertheless, speckle noise limits the analytical capabilities of this imaging tool, causing deterioration in contrast and less exact detection of tissue microstructural heterogeneity. To address this issue, we proposed OCT despeckling approach by combing it with normalization to reduce the speckle noise more effectively. The proposed method contains multiple steps including phase correlation for alignment of misaligned frames, frame averaging which minimizes speckle noise, region-wise pixels normalization that helps to normalize intensity pixels, a modified BM3D filtering to suppress the white and speckle, and contrast enhancement to improve the contrast appropriately. To establish the approach, we applied 130 distinct B-scan skin OCT images and validate and evaluate the performance using qualitatively and quantitatively. Although the output obtained by the algorithm is promising, the method is time-consuming because of a series of steps. To reduce the time complexity, we also develop a supervised deep learning model by mapping between noisy-despeckled image pairs. The effectiveness and applicability of our DL approach were assessed using 130 skin OCT B-scans from various body areas taken from 45 healthy people between the ages of 20 and 60. With the support of the experimental results, we demonstrate that our DL model is capable to normalize and despeckling OCT images simultaneously.

This study aims to make the implementation of the attenuation and backscattering study to be apply for skin tissue. Attenuation and backscatter coefficients were defined in the range 22 to 48 MHz based on the L38 transducer which has a central frequency of 30 MHz and a 60% bandwidth. The attenuation coefficient was computed using local attenuation estimation algorithms (i.e., spectral difference and spectral log difference methods). Using a reference phantom technique, we compensated the recorded backscatter spectra for system-independent measurements. This study shows the experimental and theoretical steps needed to implement a quantitative study of ultrasounds images in the skin.

Implant infections pose a serious health risk to patients, both immediately after surgery and even years later. Hard materials provide the opportunity for more potent infection clearing, but soft materials like hydrogels are more delicate and may be more susceptible to the formation of sturdy bacterial biofilms. Photodynamic therapy (PDT) is a proven technique for infectious load reduction, biofilm penetration, and sensitization to antibiotic therapies. However, evaluation is lacking in efficacy of PDT in proximity to hydrogels, based on the risks of oxidative degradation. This preliminary study assesses the effects of PDT on HEMA-DMAEMA hydrogel viscoelastic properties. PDT is applied by soaking of hydrogels in PBS containing 10 μM methylene blue and subsequent 660 nm light exposure at 9.20 𝑚𝑊/𝑐𝑚² for 30 minutes. PDT and all component controls cause reduction in values of storage modulus, loss modulus, and loss factor, but only differences in storage modulus are statistically significant as compared to baseline controls. Additionally, PDT component controls are comparable to the PDT test group, implying that photosensitizer integration and light degradation of hydrogels may be the primary source of interference. Overall, the changes noted in viscoelasticity are moderate and encourage further exploration with larger PDT doses, greater sample numbers, and testing of commercially available implant materials.

The vulva is a specific topographic region of the skin with complex morphology and a variety of functions. The combination of the urinary, reproductive and gastrointestinal systems in the same area makes the vulva susceptible to a wide range of diseases that require a multidisciplinary approach. As a result of these anatomical and physiological features, vulvar diseases are difficult to diagnose and insufficiently recognized. Multimodal optical coherence tomography (MM OCT) is an optical tool that allows real-time assessment of the structure of vulvar tissues with a resolution of 10-15 μm, and also provides label-free visualization of blood and lymphatic vessels. The advantages of the MM OCT method are its non-invasiveness, the possibility of examining a large tissue area, as well as obtaining data from various parts of the organ under study. In this paper, we use cross-polarization OCT, angiography, and lymphangiography with parallel histological examination to describe the structure of normal hairless vulvar skin. Two variants of the structure of vulvar skin were identified: skin with epidermal ridges, skin without epidermal ridges. For the first time, a change in the architecture of the microvasculature in response to a change in the structure of the epidermis has been described. Knowledge and understanding of the normal processes of change in the skin of the vulva are the main study of any pathology that occurs in this organ.