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Ulcerative colitis (UC) is a chronic inflammatory bowel disease characterized by relapsing inflammation of the large intestine. The severity of UC is often represented by the Mayo Endoscopic Subscore (MES) which quantifies mucosal disease activity from endoscopy videos. In clinical trials, an endoscopy video is assigned an MES based upon the most severe disease activity observed in the video. For this reason, severe inflammation spread throughout the colon will receive the same MES as an otherwise healthy colon with severe inflammation restricted to a small, localized segment. Therefore, the extent of disease activity throughout the large intestine, and overall response to treatment, may not be completely captured by the MES. In this work, we aim to automatically estimate UC severity for each frame in an endoscopy video to provide a higher resolution assessment of disease activity throughout the colon. Because annotating severity at the frame-level is expensive, labor-intensive, and highly subjective, we propose a novel weakly supervised, ordinal classification method to estimate frame severity from video MES labels alone. Using clinical trial data, we first achieved 0.92 and 0.90 AUC for predicting mucosal healing and remission of UC, respectively. Then, for severity estimation, we demonstrate that our models achieve substantial Cohen's Kappa agreement with ground truth MES labels, comparable to the inter-rater agreement of expert clinicians. These findings indicate that our framework could serve as a foundation for novel clinical endpoints, based on a more localized scoring system, to better evaluate UC drug efficacy in clinical trials., *Co-Senior Authors. Accepted for publication in the Special Issue Journal of Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization and presented at the MICCAI 2021 AE-CAI $|$ CARE $|$ OR 2.0 Workshop

BACKGROUND Skin hydration is essential for maintaining stratum corneum (SC) flexibility and facilitating maturation events. Moisturizers contain multiple ingredients to maintain and improve skin hydration although a complete understanding of hydration mechanisms is lacking. The ability to differentiate the source of the hydration (water from the environment or deeper skin regions) upon application of product will aid in designing more efficacious formulations. MATERIALS AND METHODS Novel confocal Raman microscopy (CRM) experiments allow us to investigate mechanisms and levels of hydration in the SC. Using deuterium oxide (D2 O) as a probe permits the differentiation of endogenous water (H2 O) from exogenous D2 O. Following topical application of D2 O, we first compare in vivo skin depth profiles with those obtained using ex vivo skin. Additional ex vivo experiments are conducted to quantify the kinetics of D2 O diffusion in the epidermis by introducing D2 O under the dermis. RESULTS Relative D2 O depth profiles from in vivo and ex vivo measurements compare well considering procedural and instrumental differences. Additional in vivo experiments where D2 O was applied following topical glycerin application increased the longevity of D2 O in the SC. Reproducible rates of D2 O diffusion as a function of depth have been established for experiments where D2 O is introduced under ex vivo skin. CONCLUSION Unique information regarding hydration mechanisms are obtained from CRM experiments using D2 O as a probe. The source and relative rates of hydration can be delineated using ex vivo skin with D2 O underneath. One can envision comparing these depth-dependent rates in the presence and absence of topically applied hydrating agents to obtain mechanistic information.

Background Sunscreens are known to protect from sun damage; however, their effects on the reversal of photodamage have been minimally investigated. Objective The aim of the prospective study was to evaluate the efficacy of a facial sun protection factor (SPF) 30 formulation for the improvement of photodamage during a 1-year use. Methods Thirty-two subjects applied a broad spectrum photostable sunscreen (SPF 30) for 52 weeks to the entire face. Assessments were conducted through dermatologist evaluations and subjects' self-assessment at baseline and then at Weeks 12, 24, 36, and 52. Results Clinical evaluations showed that all photoaging parameters improved significantly from baseline as early as Week 12 and the amelioration continued until Week 52. Skin texture, clarity, and mottled and discrete pigmentation were the most improved parameters by the end of the study (40% to 52% improvement from baseline), with 100% of subjects showing improvement in skin clarity and texture. Conclusion The daily use of a facial broad-spectrum photostable sunscreen may visibly reverse the signs of existing photodamage, in addition to preventing additional sun damage.

Two-photon fluorescence (TPF) and second harmonic generation (SHG) microscopy provide direct visualization of the skin dermal fibers in vivo. A typical method for analyzing TPF/SHG images involves averaging the image intensity and therefore disregarding the spatial distribution information. The goal of this study is to develop an algorithm to document age-related effects of the dermal matrix. TPF and SHG images were acquired from the upper inner arm, volar forearm, and cheek of female volunteers of two age groups: 20 to 30 and 60 to 80 years of age. The acquired images were analyzed for parameters relating to collagen and elastin fiber features, such as orientation and density. Both collagen and elastin fibers showed higher anisotropy in fiber orientation for the older group. The greatest difference in elastin fiber anisotropy between the two groups was found for the upper inner arm site. Elastin fiber density increased with age, whereas collagen fiber density decreased with age. The proposed analysis considers the spatial information inherent to the TPF and SHG images and provides additional insights into how the dermal fiber structure is affected by the aging process.

Background/purpose: Computational skin analysis is revolutionizing modern dermatology. Patterns extracted from image sequences enable algorithmic evaluation. Stacking multiple images to analyze pattern variation implicitly assumes that the images are aligned per-pixel. However, breathing and involuntary motion of the patient causes significant misalignment. Alignment algorithms designed for multimodal and time-lapse skin images can solve this problem. Sequences from multimodal imaging capture unique appearance features in each modality. Time-lapse image sequences capture skin appearance change over time. Methods: Multimodal skin images have been acquired under five different modalities: three in reflectance (visible, parallelpolarized, and cross-polarized) and two in fluorescence mode (UVA and blue light excitation). For time-lapse imagery, 39 images of acne lesions over a 3-month period have been collected. The method detects micro-level features like pores, wrinkles, and other skin texture markings in the acquired images. Images are automatically registered to subpixel accuracy. Results: The proposed registration approach precisely aligns multimodal and time-lapse images. Subsurface recovery from multimodal images has misregistration artefacts that can be eliminated using this approach. Registered time-lapse imaging captures the evolution of appearance of skin regions with time. Conclusion: Misalignment in skin imaging has significant impact on any quantitative or qualitative image evaluation. Micro-level features can be used to obtain highly accurate registration. Multimodal images can be organized with maximal overlap for successful registration. The resulting point-to-point alignment improves the quality of skin image analysis.

Skin appearance modeling using high resolution photography has led to advances in recognition, rendering and analysis. Computational appearance provides an exciting new opportunity for integrating macroscopic imaging and microscopic biology. Recent studies indicate that skin appearance is dependent on the unseen distribution of microbes on the skin surface, i.e. the skin microbiome. While modern sequencing methods can be used to identify microbes, these methods are costly and time-consuming. We develop a computational skin texture model to characterize image-based patterns and link them to underlying microbiome clusters. The pattern analysis uses ultraviolet and blue fluorescence multimodal skin photography. The intersection of appearance and microbiome clusters reveals a pattern of microbiome that is predictable with high accuracy based on skin appearance. Furthermore, the use of non-negative matrix factorization allows a representation of the microbiome eigenvector as a physically plausible positive distribution of bacterial components. In this paper, we present the first results in this area of predicting microbiome clusters based on computational skin texture.

Reflectance Confocal Microscopy (RCM) is a noninvasive imaging tool used in clinical dermatology and skin research, allowing real time visualization of skin structural features at different depths at a resolution comparable to that of conventional histology [1]. Currently, RCM is used to generate a rich skin image stack (about 60 to 100 images per scan) which is visually inspected by experts, a process that is tedious, time consuming and exclusively qualitative. Based on the observation that each of the skin images in the stack can be characterized as a texture, we propose a quantitative approach for automatically classifying the images in the RCM stack, as belonging to the different skin layers: stratum corneum, stratum granulosum, stratum spinosum, stratum basale, and the papillary dermis. A reduced set of images in the stack are used to generate a library of representative texture features named textons. This library is employed to characterize all the images in the stack with a corresponding texton histogram. The stack is ultimately separated into 5 different sets of images, each corresponding to different skin layers, exhibiting good correlation with expert grading. The performance of the method is tested against three RCM stacks and we generate promising classification results. The proposed method is especially valuable considering the currently scarce landscape of quantitative solutions for RCM imaging.

Background As people mature, their skin gradually presents lines, wrinkles, and folds that become more pronounced with time. Skin wrinkles are perceived as important cues in communicating information about the age of the person. Nowadays, documenting the facial appearance through imaging is prevalent in skin research, therefore detection and quantitative assessment of the degree of facial wrinkling can be a useful tool for establishing an objective baseline and for assessing benefits to facial appearance due to various dermatological treatments. However, few image-based algorithms for computationally assessing facial wrinkles are present in the literature, and those that exist have limited reliability. Methods In this work, an algorithm for automatic detection of facial wrinkles is developed, based on estimating the orientation and the frequency of elongated spatial features, captured via digital image filtering. Results The algorithm is tested against one set of clinically validated 11-point wrinkle scales present on the face. The algorithm is employed for assessing the presence of forehead furrows on a set of 100 clinically graded facial images. The proposed computational assessment correlates well with the corresponding clinical scores. Conclusion We find that the results are in better agreement with clinical scoring when the wrinkle depth information, approximated via filter responses, is combined with the wrinkle length information as opposed to the case when the two measures are considered separately.

It is known that effectiveness of acne treatment increases when the lesions are detected earlier, before they could progress into mature wound-like lesions, which lead to scarring and discoloration. However, little is known about the evolution of acne from early signs until after the lesion heals. In this work we computationally characterize the evolution of inflammatory acne lesions, based on analyzing cross-polarized images that document acne-prone facial skin over time. Taking skin images over time, and being able to follow skin features in these images present serious challenges, due to change in the appearance of skin, difficulty in repositioning the subject, involuntary movement such as breathing. A computational technique for automatic detection of lesions by separating the background normal skin from the acne lesions, based on fitting Gaussian distributions to the intensity histograms, is presented. In order to track and quantify the evolution of lesions, in terms of the degree of progress or regress, we designed a study to capture facial skin images from an acne-prone young individual, followed over the course of 3 different time points. Based on the behavior of the lesions between two consecutive time points, the automatically detected lesions are classified in four categories: new lesions, resolved lesions (i.e. lesions that disappear completely), lesions that are progressing , and lesions that are regressing (i.e. lesions in the process of healing). The classification our methods achieve correlates well with visual inspection of a trained human grader.

Nowadays, documenting the face appearance through imaging is prevalent in skin research, therefore detection and quantitative assessment of the degree of facial wrinkling is a useful tool for establishing an objective baseline and for communicating benefits to facial appearance due to cosmetic procedures or product applications. In this work, an algorithm for automatic detection of facial wrinkles is developed, based on estimating the orientation and the frequency of elongated features apparent on faces. By over-filtering the skin texture image with finely tuned oriented Gabor filters, an enhanced skin image is created. The wrinkles are detected by adaptively thresholding the enhanced image, and the degree of wrinkling is estimated based on the magnitude of the filter responses. The algorithm is tested against a clinically scored set of images of periorbital lines of different severity and we find that the proposed computational assessment correlates well with the corresponding clinical scores.

The activity of certain bacteria in skin is known to correlate to the presence of porphyrins. In particular the presence of coproporphyrin produced by P.acnes inside plugged pores has been correlated to acne vulgaris. Another porphyrin encountered in skin is protoporphyrin IX, which is produced by the body in the pathway for production of heme. In the present work, a fluorescence spectroscopy system was developed to measure the characteristic spectrum and quantify the two types of porphyrins commonly present in human facial skin. The system is comprised of a Xe lamp both for fluorescence excitation and broadband light source for diffuse reflectance measurements. A computer-controlled filter wheel enables acquisition of sequential spectra, first excited by blue light at 405 nm then followed by the broadband light source, at the same location. The diffuse reflectance spectrum was used to correct the fluorescence spectrum due to the presence of skin chromophores, such as blood and melanin. The resulting fluorescence spectra were employed for the quantification of porphyrin concentration in a population of healthy subjects. The results show great variability on the concentration of these porphyrins and further studies are being conducted to correlate them with skin conditions such as inflammation and acne vulgaris.