Joint segmentation and characterization of the dermis in 50 MHz ultrasound 2D and 3D images of the skin

Segmentation of skin layers on HFUS images using the attention mechanism

BACKGROUND AND OBJECTIVE

The fast development of imaging techniques in recent years has opened new diagnostic paths also in dermatology, where high-frequency ultrasound (HFUS) enables the visualization of superficial structures. At the same time, automated ultrasound image analysis algorithms have started to be widely described in the literature. Although the newest deep learning models can classify the images without the previous segmentation steps, they are often the first part of a computer-aided diagnosis framework that helps further measurements. For the clinical evaluation, the parameters of skin layers: entry echo, SLEB and dermis, are the most important for differential diagnosis and accurate evaluation of treatment process.

METHODS

The paper presents a novel neural network model combining contextual feature pyramid blocks with attention gates to segment skin layers accurately. In addition, a sequential model was tested that pre-segmented the entry echo layer as the most characteristic element in the skin ultrasound image. For the first time, we segmented three skin layers: the entry echo layer, SLEB, and dermis. The developed method is verified using two different HFUS image databases containing images acquired with different ultrasound machines and ultrasound probe frequencies. Measures of models' performance were proposed, assessing the percentage of cases where the model classified the whole image as background and two focusing on the SLEB layer: percentages of false positive and false negatives detections.

RESULTS

The average Dice indexes, obtained on the dataset recorded for this study, were 0.95, 0.85 and 0.93, respectively for the entry echo, SLEB and dermis. For models trained without transfer learning, proposed architectures were the only ones that detected the skin correctly every time. Both models achieved the lowest false positive (0.35% and 0%) and false negative (4.48% and 3.66%) rates during the experiments.

CONCLUSION

Contextual feature pyramid modules and attention gates allow more accurate detection and segmentation of skin layers. The results obtained are compared with other models described in the literature as efficient for HFUS image analysis, and low false positive and false negative rates speak in favor of our approach.

The usefulness of automated high frequency ultrasound image analysis in atopic dermatitis staging

The last decades have brought an interest in ultrasound applications in dermatology. Especially in the case of atopic dermatitis, where the formation of a subepidermal low echogenic band (SLEB) may serve as an independent indicator of the effects of treatment, the use of ultrasound is of particular interest. This study proposes and evaluates the computer-aided diagnosis method for assessing atopic dermatitis (AD). The fully automated image processing framework combines advanced machine learning techniques for fast, reliable, and repeatable HFUS image analysis, supporting clinical decisions. The proposed methodology comprises accurate SLEB segmentation followed by a classification step. The data set includes 20 MHz images of 80 patients diagnosed with AD according to Hanifin and Rajka criteria, which were evaluated before and after treatment. The ground true labels- clinical evaluation based on Investigator Global Assessment index (IGA score) together with ultrasound skin examination was performed. For reliable analysis, in further experiments, two experts annotated the HFUS images twice in two-week intervals. The analysis aimed to verify whether the fully automated method can classify the HFUS images at the expert level. The Dice coefficient values for segmentation reached 0.908 for SLEB and 0.936 for the entry echo layer. The accuracy of SLEB presence detection results (IGA0) is equal to 98% and slightly outperforms the experts' assessment, which reaches 96%. The overall accuracy of the AD assessment was equal to 69% (Cohen's kappa 0.78) and was comparable with the experts' assessment, ranging between 64% and 70% (Cohen's kappa 0.73-0.79). The results indicate that the automated method can be applied to AD assessment, and its combination with standard diagnosis may benefit repeatable analysis and a better understanding of the processes that take place within the skin and aid treatment monitoring.

Automatic Assessment of AK Stage Based on Dermatoscopic and HFUS Imaging-A Preliminary Study

Background: Actinic keratoses (AK) usually occur on sun-exposed areas in elderly patients with Fitzpatrick I-II skin types. Dermatoscopy and ultrasonography are two non-invasive tools helpful in examining clinically suspicious lesions. This study presents the usefulness of image-processing algorithms in AK staging based on dermatoscopic and ultrasonographic images. Methods: In 54 patients treated at the Department of Dermatology of Poznan University of Medical Sciences, clinical, dermatoscopic, and ultrasound examinations were performed. The clinico-dermoscopic AK classification was based on three-point Zalaudek scale. The ultrasound images were recorded with DermaScan C, Cortex Technology device, 20 MHz. The dataset consisted of 162 image pairs. The developed algorithm includes automated segmentation of ultrasound data utilizing a CFPNet-M model followed by handcrafted feature extraction. The dermatoscopic image analysis includes both handcrafted and convolutional neural network features, which, combined with ultrasound descriptors, are used in support vector machine-based classification. The network models were trained on public datasets. The influence of each modality on the final classification was evaluated. Results: The most promising results were obtained for the dermatoscopic analysis with the use of neural network model (accuracy 81%) and its combination with ultrasound scans (accuracy 79%). Conclusions: The application of machine learning-based algorithms in dermatoscopic and ultrasound image analysis machine learning in the staging of AKs may be beneficial in clinical practice in terms of predicting the risk of progression. Further experiments are warranted, as incorporating more images is likely to improve classification accuracy of the system.

Artificial intelligence to analyze magnetic resonance imaging in rheumatology

Rheumatic disorders present a global health challenge, marked by inflammation and damage to joints, bones, and connective tissues. Accurate, timely diagnosis and appropriate management are crucial for favorable patient outcomes. Magnetic resonance imaging (MRI) has become indispensable in rheumatology, but interpretation remains laborious and variable. Artificial intelligence (AI), including machine learning (ML) and deep learning (DL), offers a means to improve and advance MRI analysis. This review examines current AI applications in rheumatology MRI analysis, addressing diagnostic support, disease classification, activity assessment, and progression monitoring. AI demonstrates promise, with high sensitivity, specificity, and accuracy, achieving or surpassing expert performance. The review also discusses clinical implementation challenges and future research directions to enhance rheumatic disease diagnosis and management.

Computer-Aided Diagnosis Methods for High-Frequency Ultrasound Data Analysis: A Review

Over the last few decades, computer-aided diagnosis systems have become a part of clinical practice. They have the potential to assist clinicians in daily diagnostic tasks. The image processing techniques are fast, repeatable, and robust, which helps physicians to detect, classify, segment, and measure various structures. The recent rapid development of computer methods for high-frequency ultrasound image analysis opens up new diagnostic paths in dermatology, allergology, cosmetology, and aesthetic medicine. This paper, being the first in this area, presents a research overview of high-frequency ultrasound image processing techniques, which have the potential to be a part of computer-aided diagnosis systems. The reviewed methods are categorized concerning the application, utilized ultrasound device, and image data-processing type. We present the bridge between diagnostic needs and already developed solutions and discuss their limitations and future directions in high-frequency ultrasound image analysis. A search was conducted of the technical literature from 2005 to September 2022, and in total, 31 studies describing image processing methods were reviewed. The quantitative and qualitative analysis included 39 algorithms, which were selected as the most effective in this field. They were completed by 20 medical papers and define the needs and opportunities for high-frequency ultrasound application and CAD development.

In vivo evaluation of topical ascorbic acid application on skin aging by 50MHz ultrasound

Ascorbic acid (AA) is a powerful antioxidant capable of acting significantly both in the prevention and treatment of the skin aging process. One way to assess the in vivo efficacy of anti-aging treatments is by using the high-frequency ultrasound (HFUS) skin image analysis technique, a non-invasive approach that allows for a new level of evaluating the effectiveness of dermatological and cosmetic products. The aim of the present study was to assess the performance of a topical emulsion of liquid crystalline structures containing AA using the 50 MHz HFUS skin image analysis method. Twenty-five healthy female participants between 35 and 60 years old were included, all of whom randomly applied a placebo formulation and an AA-containing formulation to each forearm, once a day, for 30 days. HFUS measurements were performed before using the products (T0), two hours later (T2h), and after 30 days of use (T30d). The analyzed parameters included total skin, dermal, and epidermal echogenicity; variation and mean thickness of total skin, the epidermis and dermis; and surface roughness. Statistical analyses were performed using the Friedman test, followed by Dunn's test for comparisons of multiple means (α=0.05). A significant increase in total skin and dermal echogenicity was observed after topical AA application. Our findings suggest that collagen synthesis significantly increased after topical therapy with AA, which was responsible for the increment in dermal echogenicity. This study showed, through the HFUS technique, that the topical use of AA promoted dermal redensification after 30 days of application.

High-Frequency Ultrasound Dataset for Deep Learning-Based Image Quality Assessment

This study aims at high-frequency ultrasound image quality assessment for computer-aided diagnosis of skin. In recent decades, high-frequency ultrasound imaging opened up new opportunities in dermatology, utilizing the most recent deep learning-based algorithms for automated image analysis. An individual dermatological examination contains either a single image, a couple of pictures, or an image series acquired during the probe movement. The estimated skin parameters might depend on the probe position, orientation, or acquisition setup. Consequently, the more images analyzed, the more precise the obtained measurements. Therefore, for the automated measurements, the best choice is to acquire the image series and then analyze its parameters statistically. However, besides the correctly received images, the resulting series contains plenty of non-informative data: Images with different artifacts, noise, or the images acquired for the time stamp when the ultrasound probe has no contact with the patient skin. All of them influence further analysis, leading to misclassification or incorrect image segmentation. Therefore, an automated image selection step is crucial. To meet this need, we collected and shared 17,425 high-frequency images of the facial skin from 516 measurements of 44 patients. Two experts annotated each image as correct or not. The proposed framework utilizes a deep convolutional neural network followed by a fuzzy reasoning system to assess the acquired data’s quality automatically. Different approaches to binary and multi-class image analysis, based on the VGG-16 model, were developed and compared. The best classification results reach 91.7% accuracy for the first, and 82.3% for the second analysis, respectively.

Preliminary Clinical Experience with a Novel Optical–Ultrasound Imaging Device on Various Skin Lesions

A compact handheld skin ultrasound imaging device has been developed that uses co-registered optical and ultrasound imaging to provide diagnostic information about the full skin depth. The aim of the current work is to present the preliminary clinical results of this device. Using additional photographic, dermoscopic and ultrasonic images as reference, the images from the device were assessed in terms of the detectability of the main skin layer boundaries and characteristic image features. Combined optical-ultrasonic recordings of various types of skin lesions (melanoma, basal cell carcinoma, seborrheic keratosis, dermatofibroma, naevus, dermatitis and psoriasis) were taken with the device (N = 53) and compared with images captured with a reference portable skin ultrasound imager. The investigator and two additional independent experts performed the evaluation. The detectability of skin structures was over 90% for the epidermis, the dermis and the lesions. The morphological and echogenicity information observed for the different skin lesions were found consistent with those of the reference ultrasound device and relevant ultrasound images in the literature. The presented device was able to obtain simultaneous in-vivo optical and ultrasound images of various skin lesions. This has the potential for further investigations, including the preoperative planning of skin cancer treatment.

Automated segmentation of epidermis in high-frequency ultrasound of pathological skin using a cascade of DeepLab v3+ networks and fuzzy connectedness

This study proposes a novel, fully automated framework for epidermal layer segmentation in different skin diseases based on 75 MHz high-frequency ultrasound (HFUS) image data. A robust epidermis segmentation is a vital first step to detect changes in thickness, shape, and intensity and therefore support diagnosis and treatment monitoring in inflammatory and neoplastic skin lesions. Our framework links deep learning and fuzzy connectedness for image analysis. It consists of a cascade of two DeepLab v3+ models with a ResNet-50 backbone and a fuzzy connectedness analysis module for fine segmentation. Both deep models are pre-trained on the ImageNet dataset and subjected to transfer learning using our HFUS database of 580 images with atopic dermatitis, psoriasis and non-melanocytic skin tumors. The first deep model is used to detect the appropriate region of interest, while the second stands for the main segmentation procedure. We use the softmax layer of the latter twofold to prepare the input data for fuzzy connectedness analysis: as a reservoir of seed points and a direct contribution to the input image. In the experiments, we analyze different configurations of the framework, including region of interest detection, deep model backbones and training loss functions, or fuzzy connectedness analysis with parameter settings. We also use the Dice index and epidermis thickness to compare our results to state-of-the-art approaches. The Dice index of 0.919 yielded by our model over the entire dataset (and exceeding 0.93 in inflammatory diseases) proves its superiority over the other methods.

Deep Learning-Based High-Frequency Ultrasound Skin Image Classification with Multicriteria Model Evaluation

This study presents the first application of convolutional neural networks to high-frequency ultrasound skin image classification. This type of imaging opens up new opportunities in dermatology, showing inflammatory diseases such as atopic dermatitis, psoriasis, or skin lesions. We collected a database of 631 images with healthy skin and different skin pathologies to train and assess all stages of the methodology. The proposed framework starts with the segmentation of the epidermal layer using a DeepLab v3+ model with a pre-trained Xception backbone. We employ transfer learning to train the segmentation model for two purposes: to extract the region of interest for classification and to prepare the skin layer map for classification confidence estimation. For classification, we train five models in different input data modes and data augmentation setups. We also introduce a classification confidence level to evaluate the deep model’s reliability. The measure combines our skin layer map with the heatmap produced by the Grad-CAM technique designed to indicate image regions used by the deep model to make a classification decision. Moreover, we propose a multicriteria model evaluation measure to select the optimal model in terms of classification accuracy, confidence, and test dataset size. The experiments described in the paper show that the DenseNet-201 model fed with the extracted region of interest produces the most reliable and accurate results.

Deep learning approach to skin layers segmentation in inflammatory dermatoses

Monitoring skin layers with medical imaging is critical to diagnosing and treating patients with chronic inflammatory skin diseases. The high-frequency ultrasound (HFUS) makes it possible to monitor skin condition in different dermatoses. Accurate and reliable segmentation of skin layers in patients with atopic dermatitis or psoriasis enables the assessment of the treatment effect by the layer thickness measurements. The epidermis and the subepidermal low echogenic band (SLEB) are the most important for further diagnosis since their appearance is an indicator of different skin problems. In medical practice, the analysis, including segmentation, is usually performed manually by the physician with all drawbacks of such an approach, e.g., extensive time consumption and lack of repeatability. Recently, HFUS becomes common in dermatological practice, yet it is barely supported by the development of automated analysis tools. To meet the need for skin layer segmentation and measurement, we developed an automated segmentation method of both epidermis and SLEB layers. It consists of a fuzzy c-means clustering-based preprocessing step followed by a U-shaped convolutional neural network. The network employs batch normalization layers adjusting and scaling the activation to make the segmentation more robust. The obtained segmentation results are verified and compared to the current state-of-the-art methods addressing the skin layer segmentation. The obtained Dice coefficient equal to 0.87 and 0.83 for the epidermis and SLEB, respectively, proves the developed framework's efficiency, outperforming the other approaches.

High-frequency ultrasound as a scientific tool for skin imaging analysis

Ultrasonic imaging is one of the most important diagnostic tools in clinical medicine due to its cost, availability and good correlation with pathological results. High-frequency ultrasound (HFUS) is a technique used in skin science that has been little explored, especially in comparison with other sites and imaging techniques. HFUS shows real-time images of the skin layers, appendages and skin lesions in vivo and can significantly contribute to advances in skin science. This review summarizes the potential applications of HFUS in dermatology and cosmetology, with a focus on quantitative tools that can be used to assess various skin conditions. Our findings showed that HFUS imaging is a reproducible and powerful tool for the diagnosis, clinical management and therapy monitoring of skin conditions. It is also a helpful tool for assessing the performance of dermatological products. This technique may eventually become essential for evaluating the performance of dermatological and cosmetic products.

Characterization of skin aging through high-frequency ultrasound imaging as a technique for evaluating the effectiveness of anti-aging products and procedures: A review

INTRODUCTION

High-frequency ultrasound skin imaging analysis (HFUS) is a non-invasive technique that allows a unique approach to the analysis of skin aging, as well as in evaluating the effectiveness of dermatological and cosmetic products, especially for skin rejuvenation.

OBJECTIVE

To describe the impact of skin aging and different anti-aging strategies from the perspective of high-frequency ultrasound.

METHODS

A bibliographic survey was carried out, selecting relevant articles that evaluated the characterization of the skin features from different points of view such as gender (male and female), age (young skin and mature skin), and ethnicity, in addition to individual variations between body regions and daily variations.

RESULTS

Some studies also evaluated the impact of cosmetic treatments and esthetic procedures in the skin. Parameters such as dermal thickness, echogenicity, skin texture, and subepidermal low-echogenic band (SLEB) were analyzed. It can be concluded that there is a trend, although not unanimous in the consequences of aging on the skin, being different between men and women, plus the individual nuances resulted from each one's lifestyle and exposure to the sun.

CONCLUSION

As for the technique, it is concluded that high-frequency ultrasound is an important evaluative alternative for dermatological studies and the effectiveness of anti-aging products and treatments.

Automated seeding for ultrasound skin lesion segmentation

The segmentation of cancer-suspicious skin lesions using ultrasound may help their differential diagnosis and treatment planning. Active contour models (ACM) require an initial seed, which when manually chosen may cause variations in segmentation accuracy. Fully-automated skin segmentation typically employs layer-by-layer segmentation using a combination of methods; however, such segmentation has not yet been applied on cancerous lesions. In the current work, fully automated segmentation is achieved in two steps: an automated seeding (AS) step using a layer-by-layer method followed by a growing step using an ACM. The method was tested on images of nevi, melanomas, and basal cell carcinomas from two ultrasound imaging systems (N=60), with all lesions being successfully located. For the seeding step, manual seeding (MS) was used as a reference. AS approached the accuracy of MS when the latter used an optimal bounding rectangle based on the ground truth (Sørensen-Dice coefficient (SDC) of 72.3 vs 74.6, respectively). The effect of varying the manual seed was also investigated; a 0.7 decrease in seed height and width caused a mean SDC of 54.6. The results show the robustness of automated seeding for skin lesion segmentation.

Use of artificial intelligence in imaging in rheumatology - current status and future perspectives

After decades of basic research with many setbacks, artificial intelligence (AI) has recently obtained significant breakthroughs, enabling computer programs to outperform human interpretation of medical images in very specific areas. After this shock wave that probably exceeds the impact of the first AI victory of defeating the world chess champion in 1997, some reflection may be appropriate on the consequences for clinical imaging in rheumatology. In this narrative review, a short explanation is given about the various AI techniques, including 'deep learning', and how these have been applied to rheumatological imaging, focussing on rheumatoid arthritis and systemic sclerosis as examples. By discussing the principle limitations of AI and deep learning, this review aims to give insight into possible future perspectives of AI applications in rheumatology.

Multi-Grid Phase Field Skin Tumor Segmentation in 3D Ultrasound Images

The aim of this paper is to present a new method for skin tumor segmentation in the 3D ultrasound images. We consider a variational formulation, the energy of which combines a diffuse interface phase field model (regularization term) and a log-likelihood computed using nonparametric estimates (data attachment term). We propose a multi-grid implementation with the exact solutions which has the advantage to avoid space discretization and numerical instabilities. The resulting algorithm is simple and easy to implement in multi-dimensions. Concerning applications, we focus on skin tumor segmentation. The clinical dataset used for the experiments is composed of 12 images with the ground truth given by a dermatologist. Comparisons with the reference methods show that the proposed method is more robust to the choice of the volume initialization. Moreover, thanks to the flexibility introduced by the diffuse interface, the sensitivity increases by 12% if the initialization is inside the lesion, and the Dice index increases by 59%, if the initialization covers the entire lesion. These results show that this new method is well designed to tackle the problem of underestimation of tumor volumes.