Nevus sebaceus of Jadassohn - high frequency ultrasound imaging and videodermoscopy examination. Case presentation

Borak

2022

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.

“…The offered penetration depth ranges are from 1.6 to 3.2 mm with a maximum axial resolution of 16

m at 100 MHz for the DUB 100-12 bit device, and from 3.2 to 15 mm with a maximum axial resolution of 21

Section: High-frequency Ultrasound Imagingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Borak

2022

“…During the last decades, high-frequency ultrasound (HFUS, >20 MHz) has opened up new diagnostic paths in skin analysis, enabling visualization and diagnosis of superficial structures [1,2]. Therefore, it has gained popularity in various areas of medical diagnostics [3,4] and is now commonly used in medical practice [5].…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, it has gained popularity in various areas of medical diagnostics [3,4] and is now commonly used in medical practice [5]. In oncology, it helps in the determination of skin tumor depth, prognosis, and surgical planning [1,6], enabling differentiation between melanoma, benign nevi, and seborrheic keratoses [7]. Heibel et al [6] presented the HFUS as a reliable method with perfect intrahand interreproducibility for the measurement of melanoma depth in vivo.…”

Section: Introductionmentioning

confidence: 99%

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

Juszczyk

Piejko

et al. 2022

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.

“…US at frequencies below 100 MHz is now commonly used in medical practice [ 3 ], and the >100 MHz probes are constantly being designed [ 4 ]. Improving spatial resolution of acquired images, the higher frequency of the US probe enables clear visualization of superficial structures such as the fat layer, the muscle layer, blood vessels, hair follicles, and skin appendages [ 2 , 5 , 6 , 7 ]. HFUS is used for healthy skin analysis, where skin thickness is inversely proportional to age, and due to increased collagen production connected with aging, echogenicity tends to increase too [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

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

Badura

Korzekwa

et al. 2021

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.