Ultrasound features of high-risk basal cell carcinoma: a systematic review.

Basal Cell Carcinoma (BCC) is the most prevalent skin cancer and continues to witness a surge in incidence rates. The categorization of BCC subtypes into low or high risk, guided by recurrence and invasiveness metrics, underscores the need for precise differentiation. While the punch biopsy remains the gold standard for diagnosis, its invasiveness prompts a need for non-invasive alternatives. Ultrasound (US) has emerged as a noteworthy candidate, gaining momentum in its potential to offer a less intrusive diagnostic approach. We conducted a systematic review regarding features of the high-risk subtypes of BCC on US. A thorough literature search of PubMed Medline, Embase, and CINAHL databases was conducted according to PRISMA guidelines and a total of nine studies meeting our inclusion criteria were included in this review. Evidence is still nascent but US features such as lesional shape, depth, hyperechoic spots, and color doppler may be helpful in differentiating high-risk BCC subtypes. However, further prospective studies with standardized interventions and outcome measures are required.

A novel multi-task learning network for skin lesion classification based on multi-modal clues and label-level fusion.

In this paper, we propose a multi-task learning (MTL) network based on the label-level fusion of metadata and hand-crafted features by unsupervised clustering to generate new clustering labels as an optimization goal. We propose a MTL module (MTLM) that incorporates an attention mechanism to enable the model to learn more integrated, variable information. We propose a dynamic strategy to adjust the loss weights of different tasks, and trade off the contributions of multiple branches. Instead of feature-level fusion, we propose label-level fusion and combine the results of our proposed MTLM with the results of the image classification network to achieve better lesion prediction on multiple dermatological datasets. We verify the effectiveness of the proposed model by quantitative and qualitative measures. The MTL network using multi-modal clues and label-level fusion can yield the significant performance improvement for skin lesion classification.

Segmentation and quantitative analysis of optical coherence tomography (OCT) images of laser burned skin based on deep learning.

Evaluation of skin recovery is an important step in the treatment of burns. However, conventional methods only observe the surface of the skin and cannot quantify the injury volume. Optical coherence tomography (OCT) is a non-invasive, non-contact, real-time technique. Swept source OCT uses near infrared light and analyzes the intensity of light echo at different depths to generate images from optical interference signals. To quantify the dynamic recovery of skin burns over time, laser induced skin burns in mice were evaluated using deep learning of Swept source OCT images. A laser-induced mouse skin thermal injury model was established in thirty Kunming mice, and OCT images of normal and burned areas of mouse skin were acquired at day 0, day 1, day 3, day 7, and day 14 after laser irradiation. This resulted in 7000 normal and 1400 burn B-scan images which were divided into training, validation, and test sets at 8:1.5:0.5 ratio for the normal data and 8:1:1 for the burn data. Normal images were manually annotated, and the deep learning U-Net model (verified with PSPNe and HRNet models) was used to segment the skin into three layers: the dermal epidermal layer, subcutaneous fat layer, and muscle layer. For the burn images, the models were trained to segment just the damaged area. Three-dimensional reconstruction technology was then used to reconstruct the damaged tissue and calculate the damaged tissue volume. The average IoU value and f-score of the normal tissue layer U-Net segmentation model were 0.876 and 0.934 respectively. The IoU value of the burn area segmentation model reached 0.907 and f-score value reached 0.951. Compared with manual labeling, the U-Net model was faster with higher accuracy for skin stratification. OCT and U-Net segmentation can provide rapid and accurate analysis of tissue changes and clinical guidance in the treatment of burns.

Ultrafast 3-D Transcutaneous Super Resolution Ultrasound Using Row-Column Array Specific Coherence-Based Beamforming and Rolling Acoustic Sub-aperture Processing: In Vitro, in Rabbit and in Human Study.

OBJECTIVE: This study aimed to realise 3-D super-resolution ultrasound imaging transcutaneously with a row-column array which has far fewer independent electronic channels and a wider field of view than typical fully addressed 2-D matrix arrays. The in vivo image quality of the row-column array is generally poor, particularly when imaging non-invasively. This study aimed to develop a suite of image formation and post-processing methods to improve image quality and demonstrate the feasibility of ultrasound localisation microscopy using a row-column array, transcutaneously on a rabbit model and in a human. METHODS: To achieve this, a processing pipeline was developed which included a new type of rolling window image reconstruction, which integrated a row-column array specific coherence-based beamforming technique with acoustic sub-aperture processing. This and other processing steps reduced the 'secondary' lobe artefacts, and noise and increased the effective frame rate, thereby enabling ultrasound localisation images to be produced. RESULTS: Using an in vitro cross tube, it was found that the procedure reduced the percentage of 'false' locations from ∼26% to ∼15% compared to orthogonal plane wave compounding. Additionally, it was found that the noise could be reduced by ∼7 dB and the effective frame rate was increased to over 4000 fps. In vivo, ultrasound localisation microscopy was used to produce images non-invasively of a rabbit kidney and a human thyroid. CONCLUSION: It has been demonstrated that the proposed methods using a row-column array can produce large field of view super-resolution microvascular images in vivo and in a human non-invasively.

Optimizing deep learning-based segmentation of densely packed cells using cell surface markers.

BACKGROUND: Spatial molecular profiling depends on accurate cell segmentation. Identification and quantitation of individual cells in dense tissues, e.g. highly inflamed tissue caused by viral infection or immune reaction, remains a challenge. METHODS: We first assess the performance of 18 deep learning-based cell segmentation models, either pre-trained or trained by us using two public image sets, on a set of immunofluorescence images stained with immune cell surface markers in skin tissue obtained during human herpes simplex virus (HSV) infection. We then further train eight of these models using up to 10,000+ training instances from the current image set. Finally, we seek to improve performance by tuning parameters of the most successful method from the previous step. RESULTS: The best model before fine-tuning achieves a mean Average Precision (mAP) of 0.516. Prediction performance improves substantially after training. The best model is the cyto model from Cellpose. After training, it achieves an mAP of 0.694; with further parameter tuning, the mAP reaches 0.711. CONCLUSION: Selecting the best model among the existing approaches and further training the model with images of interest produce the most gain in prediction performance. The performance of the resulting model compares favorably to human performance. The imperfection of the final model performance can be attributed to the moderate signal-to-noise ratio in the imageset.

Dose-toxicity surface histogram-based prediction of radiation dermatitis severity and shape.

Objective.This study aimed to develop a new approach to predict radiation dermatitis (RD) by using the skin dose distribution in the actual area of RD occurrence to determine the predictive dose by grade.Approach.Twenty-three patients with head and neck cancer treated with volumetric modulated arc therapy were prospectively and retrospectively enrolled. A framework was developed to segment the RD occurrence area in skin photography by matching the skin surface image obtained using a 3D camera with the skin dose distribution. RD predictive doses were generated using the dose-toxicity surface histogram (DTH) calculated from the skin dose distribution within the segmented RD regions classified by severity. We then evaluated whether the developed DTH-based framework could visually predict RD grades and their occurrence areas and shapes according to severity.Main results.The developed framework successfully generated the DTH for three different RD severities: faint erythema (grade 1), dry desquamation (grade 2), and moist desquamation (grade 3); 48 DTHs were obtained from 23 patients: 23, 22, and 3 DTHs for grades 1, 2, and 3, respectively. The RD predictive doses determined using DTHs were 28.9 Gy, 38.1 Gy, and 54.3 Gy for grades 1, 2, and 3, respectively. The estimated RD occurrence area visualized by the DTH-based RD predictive dose showed acceptable agreement for all grades compared with the actual RD region in the patient. The predicted RD grade was accurate, except in two patients.Significance. The developed DTH-based framework can classify and determine RD predictive doses according to severity and visually predict the occurrence area and shape of different RD severities. The proposed approach can be used to predict the severity and shape of potential RD in patients and thus aid physicians in decision making.

Multi-scale feature fusion and class weight loss for skin lesion classification.

Skin cancer is one of the common types of cancer. It spreads quickly and is not easy to detect in the early stages, posing a major threat to human health. In recent years, deep learning methods have attracted widespread attention for skin cancer detection in dermoscopic images. However, training a practical classifier becomes highly challenging due to inter-class similarity and intra-class variation in skin lesion images. To address these problems, we propose a multi-scale fusion structure that combines shallow and deep features for more accurate classification. Simultaneously, we implement three approaches to the problem of class imbalance: class weighting, label smoothing, and resampling. In addition, the HAM10000_RE dataset strips out hair features to demonstrate the role of hair features in the classification process. We demonstrate that the region of interest is the most critical classification feature for the HAM10000_SE dataset, which segments lesion regions. We evaluated the effectiveness of our model using the HAM10000 and ISIC2019 dataset. The results showed that this method performed well in dermoscopic classification tasks, with ACC and AUC of 94.0% and 99.3%, on the HAM10000 dataset and ACC of 89.8% for the ISIC2019 dataset. The overall performance of our model is excellent in comparison to state-of-the-art models.

The SPECTRAL Perfusion Arm Clamping dAtaset (SPECTRALPACA) for video-rate functional imaging of the skin.

Spectral imaging has the potential to become a key technique in interventional medicine as it unveils much richer optical information compared to conventional RBG (red, green, and blue)-based imaging. Thus allowing for high-resolution functional tissue analysis in real time. Its higher information density particularly shows promise for the development of powerful perfusion monitoring methods for clinical use. However, even though in vivo validation of such methods is crucial for their clinical translation, the biomedical field suffers from a lack of publicly available datasets for this purpose. Closing this gap, we generated the SPECTRAL Perfusion Arm Clamping dAtaset (SPECTRALPACA). It comprises ten spectral videos (∼20 Hz, approx. 20,000 frames each) systematically recorded of the hands of ten healthy human participants in different functional states. We paired each spectral video with concisely tracked regions of interest, and corresponding diffuse reflectance measurements recorded with a spectrometer. Providing the first openly accessible in human spectral video dataset for perfusion monitoring, our work facilitates the development and validation of new functional imaging methods.

Structural neuroimaging of skin-picking disorder.

BACKGROUND: Skin-picking disorder (SPD) is conceptualized as an obsessive-compulsive and related disorder (OCRD). Patients with SPD excessively manipulate their skin, which leads to skin lesions, psychological distress, and functional impairment. The neuroanatomical facets of this disorder are still poorly understood. METHODS: A total of 220 participants (123 patients with a primary diagnosis of SPD and 97 healthy controls; mean age = 30 years, 80% female) were recruited for a voxel-based morphometry (VBM) study. VBM data were compared between patients and controls, and between three SPD subgroups, each characterized by a distinct age of symptom onset (before puberty, during puberty, adulthood). RESULTS: Relative to the healthy comparison group, patients with SPD had significantly less grey matter volume (GMV) in regions of interest (ROIs: insula, orbitofrontal cortex, pallidum, cerebellum, supramarginal gyrus) and in the frontal pole and occipital regions (whole-brain findings). Early onset of symptoms (before puberty) was associated with elevated levels of focused skin-picking, in addition to less GMV in specific ROIs (insula, orbitofrontal cortex) as well as in paracingulate/ superior temporal regions (whole-brain findings). CONCLUSIONS: SPD-related reductions in GMV were identified in brain regions involved in interoception, emotion regulation, and motor control. This partially aligns with findings for OCD. The detection of different age-of-onset groups based on clinical as well as morphometric data points to the heterogeneity of the disorder and warrants further investigation.

Automated Prediction of Malignant Melanoma using Two-Stage Convolutional Neural Network.

PURPOSE: A skin lesion refers to an area of the skin that exhibits anomalous growth or distinctive visual characteristics compared to the surrounding skin. Benign skin lesions are noncancerous and generally pose no threat. These irregular skin growths can vary in appearance. On the other hand, malignant skin lesions correspond to skin cancer, which happens to be the most prevalent form of cancer in the United States. Skin cancer involves the unusual proliferation of skin cells anywhere on the body. The conventional method for detecting skin cancer is relatively more painful. METHODS: This work involves the automated prediction of skin cancer and its types using two stage Convolutional Neural Network (CNN). The first stage of CNN extracts low level features and second stage extracts high level features. Feature selection is done using these two CNN and ABCD (Asymmetry, Border irregularity, Colour variation, and Diameter) technique. The features extracted from the two CNNs are fused with ABCD features and fed into classifiers for the final prediction. The classifiers employed in this work include ensemble learning methods such as gradient boosting and XG boost, as well as machine learning classifiers like decision trees and logistic regression. This methodology is evaluated using the International Skin Imaging Collaboration (ISIC) 2018 and 2019 dataset. RESULTS: As a result, the first stage CNN which is used for creation of new dataset achieved an accuracy of 97.92%. Second stage CNN which is used for feature selection achieved an accuracy of 98.86%. Classification results are obtained for both with and without fusion of features. CONCLUSION: Therefore, two stage prediction model achieved better results with feature fusion.

Hydradermabrasion through the lens of Line-Field Confocal Optical Coherence Tomography.

BACKGROUND: Hydradermabrasion, also known as "HydraFacial," is an exfoliative cosmetic procedure for skin rejuvenation that has gained popularity. Despite its increasing popularity, clinical studies validating its efficacy with non-invasive assessment of histological changes to the skin, are scarce. In this study, we used Line-Field Confocal Optical Coherence Tomography (LC-OCT), an optical imaging device, to non-invasively visualize microscopic changes to skin anatomy after hydradermabrasion treatment. MATERIALS/METHODS: Eight volunteers (Fitzpatrick skin types II-V) were recruited for this study. Images, using LC-OCT (DeepLive, DAMAE medical) were obtained before and after hydradermabrasion and at 2 weeks post-treatment. A commercially available hydradermabrasion device was utilized to perform the dermabrasion. RESULTS: In the epidermis, initially, a decrease in the average thickness of the stratum corneum, from 9.42 to 6.67 µm was visualized in LC-OCT images after hydradermabrasion. However, at 2 weeks of follow-up, the average stratum corneum thickness was 9.75 µm, resulting in an overall increase in the average thickness after treatment. Improved homogenization of the stratum corneum and decreased number of undulations in the epidermis post-treatment were also visualized. In all the subjects, the superficial dermis appeared stretched, which returned to baseline by the 2-week follow-up. At the 2-week follow-up, there were no visible differences in the quality and quantity of collagen fibers in the dermis. CONCLUSION: In our study, LC-OCT images of the epidermis and dermis demonstrated microscopic features of skin rejuvenation when treated with hydradermabrasion. Thus, not only highlighting the efficacy of hydradermabrasion but also the potential of LC-OCT to serve as a tool for visualizing the microscopic effects of cosmetic procedures on skin anatomy.

A novel SpaSA based hyper-parameter optimized FCEDN with adaptive CNN classification for skin cancer detection.

Skin cancer is the most prevalent kind of cancer in people. It is estimated that more than 1 million people get skin cancer every year in the world. The effectiveness of the disease's therapy is significantly impacted by early identification of this illness. Preprocessing is the initial detecting stage in enhancing the quality of skin images by removing undesired background noise and objects. This study aims is to compile preprocessing techniques for skin cancer imaging that are currently accessible. Researchers looking into automated skin cancer diagnosis might use this article as an excellent place to start. The fully convolutional encoder-decoder network and Sparrow search algorithm (FCEDN-SpaSA) are proposed in this study for the segmentation of dermoscopic images. The individual wolf method and the ensemble ghosting technique are integrated to generate a neighbour-based search strategy in SpaSA for stressing the correct balance between navigation and exploitation. The classification procedure is accomplished by using an adaptive CNN technique to discriminate between normal skin and malignant skin lesions suggestive of disease. Our method provides classification accuracies comparable to commonly used incremental learning techniques while using less energy, storage space, memory access, and training time (only network updates with new training samples, no network sharing). In a simulation, the segmentation performance of the proposed technique on the ISBI 2017, ISIC 2018, and PH2 datasets reached accuracies of 95.28%, 95.89%, 92.70%, and 98.78%, respectively, on the same dataset and assessed the classification performance. It is accurate 91.67% of the time. The efficiency of the suggested strategy is demonstrated through comparisons with cutting-edge methodologies.

Parameter-based transfer learning for severity classification of atopic dermatitis using hyperspectral imaging.

BACKGROUND/PURPOSE: Because atopic dermatitis (AD) is a chronic inflammatory skin condition that causes structural changes, there is a growing need for noninvasive research methods to evaluate this condition. Hyperspectral imaging (HSI) captures skin structure features by exploiting light wavelength variations in penetration depth. In this study, parameter-based transfer learning was deployed to classify the severity of AD using HSI. Therefore, we aimed to obtain an optimal combination of classification results from the four models after constructing different source- and target-domain datasets. METHODS: We designated psoriasis, skin cancer, eczema, and AD datasets as the source datasets, and the set of images acquired via hyperspectral camera as the target dataset for wavelength-specific AD classification. We compared the severity classification performances of 96 combinations of sources, models, and targets. RESULTS: The highest classification performance of 83% was achieved when ResNet50 was trained on the augmented psoriasis dataset as the source, with the resulting parameters used to train the model on the target Near-infrared radiation (NIR) dataset. The second highest classification accuracy of 81% was achieved when ResNet50 was trained on the unaugmented psoriasis dataset as the source, with the resulting parameters used to train the model on the target R dataset. ResNet50 demonstrated potential as a generalized model for both the source and target data, also confirming that the psoriasis dataset is an effective training resource. CONCLUSION: The present study not only demonstrates the feasibility of the severity classification of AD based on hyperspectral images, but also showcases combinations and research scalability for domain exploration.

Mapping cutaneous field carcinogenesis of nonmelanoma skin cancer using mesoscopic imaging of pro-inflammation cues.

Nonmelanoma skin cancers remain the most widely diagnosed types of cancers globally. Thus, for optimal patient management, it has become imperative that we focus our efforts on the detection and monitoring of cutaneous field carcinogenesis. The concept of field cancerization (or field carcinogenesis), introduced by Slaughter in 1953 in the context of oral cancer, suggests that invasive cancer may emerge from a molecularly and genetically altered field affecting a substantial area of underlying tissue including the skin. A carcinogenic field alteration, present in precancerous tissue over a relatively large area, is not easily detected by routine visualization. Conventional dermoscopy and microscopy imaging are often limited in assessing the entire carcinogenic landscape. Recent efforts have suggested the use of noninvasive mesoscopic (between microscopic and macroscopic) optical imaging methods that can detect chronic inflammatory features to identify pre-cancerous and cancerous angiogenic changes in tissue microenvironments. This concise review covers major types of mesoscopic optical imaging modalities capable of assessing pro-inflammatory cues by quantifying blood haemoglobin parameters and hemodynamics. Importantly, these imaging modalities demonstrate the ability to detect angiogenesis and inflammation associated with actinically damaged skin. Representative experimental preclinical and human clinical studies using these imaging methods provide biological and clinical relevance to cutaneous field carcinogenesis in altered tissue microenvironments in the apparently normal epidermis and dermis. Overall, mesoscopic optical imaging modalities assessing chronic inflammatory hyperemia can enhance the understanding of cutaneous field carcinogenesis, offer a window of intervention and monitoring for actinic keratoses and nonmelanoma skin cancers and maximise currently available treatment options.

Customizable Presentation Attack Detection for Improved Resilience of Biometric Applications Using Near-Infrared Skin Detection.

Due to their user-friendliness and reliability, biometric systems have taken a central role in everyday digital identity management for all kinds of private, financial and governmental applications with increasing security requirements. A central security aspect of unsupervised biometric authentication systems is the presentation attack detection (PAD) mechanism, which defines the robustness to fake or altered biometric features. Artifacts like photos, artificial fingers, face masks and fake iris contact lenses are a general security threat for all biometric modalities. The Biometric Evaluation Center of the Institute of Safety and Security Research (ISF) at the University of Applied Sciences Bonn-Rhein-Sieg has specialized in the development of a near-infrared (NIR)-based contact-less detection technology that can distinguish between human skin and most artifact materials. This technology is highly adaptable and has already been successfully integrated into fingerprint scanners, face recognition devices and hand vein scanners. In this work, we introduce a cutting-edge, miniaturized near-infrared presentation attack detection (NIR-PAD) device. It includes an innovative signal processing chain and an integrated distance measurement feature to boost both reliability and resilience. We detail the device's modular configuration and conceptual decisions, highlighting its suitability as a versatile platform for sensor fusion and seamless integration into future biometric systems. This paper elucidates the technological foundations and conceptual framework of the NIR-PAD reference platform, alongside an exploration of its potential applications and prospective enhancements.

Multi-frequency spatial frequency domain imaging: a depth-resolved optical scattering model to isolate scattering contrast in thin layers of skin.

Significance: Current methods for wound healing assessment rely on visual inspection, which gives qualitative information. Optical methods allow for quantitative non-invasive measurements of optical properties relevant to wound healing. Aim: Spatial frequency domain imaging (SFDI) measures the absorption and reduced scattering coefficients of tissue. Typically, SFDI assumes homogeneous tissue; however, layered structures are present in skin. We evaluate a multi-frequency approach to process SFDI data that estimates depth-specific scattering over differing penetration depths. Approach: Multi-layer phantoms were manufactured to mimic wound healing scattering contrast in depth. An SFDI device imaged these phantoms and data were processed according to our multi-frequency approach. The depth sensitive data were then compared with a two-layer scattering model based on light fluence. Results: The measured scattering from the phantoms changed with spatial frequency as our two-layer model predicted. The performance of two δ-P1 models solutions for SFDI was consistently better than the standard diffusion approximation. Conclusions: We presented an approach to process SFDI data that returns depth-resolved scattering contrast. This method allows for the implementation of layered optical models that more accurately represent physiologic parameters in thin tissue structures as in wound healing.

Quantifying Bone and Skin Movement in the Residual Limb-Socket Interface of Individuals With Transtibial Limb Loss Using Dynamic Stereo X-Ray: Protocol for a Lower Limb Loss Cadaver and Clinical Study.

BACKGROUND: Relative motion between the residual limb and socket in individuals with transtibial limb loss can lead to substantial consequences that limit mobility. Although assessments of the relative motion between the residual limb and socket have been performed, there remains a substantial gap in understanding the complex mechanics of the residual limb-socket interface during dynamic activities that limits the ability to improve socket design. However, dynamic stereo x-ray (DSX) is an advanced imaging technology that can quantify 3D bone movement and skin deformation inside a socket during dynamic activities. OBJECTIVE: This study aims to develop analytical tools using DSX to quantify the dynamic, in vivo kinematics between the residual limb and socket and the mechanism of residual tissue deformation. METHODS: A lower limb cadaver study will first be performed to optimize the placement of an array of radiopaque beads and markers on the socket, liner, and skin to simultaneously assess dynamic tibial movement and residual tissue and liner deformation. Five cadaver limbs will be used in an iterative process to develop an optimal marker setup. Stance phase gait will be simulated during each session to induce bone movement and skin and liner deformation. The number, shape, size, and placement of each marker will be evaluated after each session to refine the marker set. Once an optimal marker setup is identified, 21 participants with transtibial limb loss will be fitted with a socket capable of being suspended via both elevated vacuum and traditional suction. Participants will undergo a 4-week acclimation period and then be tested in the DSX system to track tibial, skin, and liner motion under both suspension techniques during 3 activities: treadmill walking at a self-selected speed, at a walking speed 10% faster, and during a step-down movement. The performance of the 2 suspension techniques will be evaluated by quantifying the 3D bone movement of the residual tibia with respect to the socket and quantifying liner and skin deformation at the socket-residuum interface. RESULTS: This study was funded in October 2021. Cadaver testing began in January 2023. Enrollment began in February 2024. Data collection is expected to conclude in December 2025. The initial dissemination of results is expected in November 2026. CONCLUSIONS: The successful completion of this study will help develop analytical methods for the accurate assessment of residual limb-socket motion. The results will significantly advance the understanding of the complex biomechanical interactions between the residual limb and the socket, which can aid in evidence-based clinical practice and socket prescription guidelines. This critical foundational information can aid in the development of future socket technology that has the potential to reduce secondary comorbidities that result from complications of poor prosthesis load transmission. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/57329.

Visualization of intradermal blood vessel structures by dual-wavelength photoacoustic microscopy and characterization of three-dimensional construction of livedo-racemosa in cutaneous polyarteritis nodosa.

BACKGROUND: Photoacoustic microscopy is expected to have clinical applications as a noninvasive and three-dimensional (3D) method of observing intradermal structures. OBJECTIVE: Investigate the applicability of a photoacoustic microscope equipped with two types of pulsed lasers that can simultaneously recognize hemoglobin and melanin. METHODS: 16 skin lesions including erythema, pigmented lesions, vitiligo and purpura, were analyzed to visualize 3D structure of melanin granule distribution and dermal blood vessels. 13 cases of livedo racemosa in cutaneous polyarteritis nodosa (cPN) were further analyzed to visualize the 3D structure of dermal blood vessels in detail. Vascular structure was also analyzed in the biopsy specimens obtained from tender indurated erythema of cPN by CD34 immunostaining. RESULTS: Hemoglobin-recognition signal clearly visualized the 3D structure of dermal blood vessels and melanin-recognition signal was consistently reduced in vitiligo. In livedo racemosa, the hemoglobin-recognition signal revealed a relatively thick and large reticular structure in the deeper layers that became denser and finer toward the upper layers. The numerical analysis revealed that the number of dermal blood vessels was 1.29-fold higher (p<0.05) in the deeper region of the lesion than that of normal skin. The CD34 immunohistochemical analysis in tender indurated erythema revealed an increased number of dermal vessels compared with normal skin in 88.9% (8/9) of the cases, suggesting that vascular network remodeling had occurred in cPN. CONCLUSION: The photoacoustic system has an advantage in noninvasively detecting dermal blood vessel structures that are difficult to recognize by two-dimensional histopathology specimen examination and is worth evaluating in various skin diseases.