Data for: Deep Learning Approach to Skin Layers Segmentation in Inflammatory Dermatoses

Czajkowska, Joanna; Badura, Paweł; Płatkowska-Szczerek, Anna; Korzekwa, Szymon

doi:10.17632/5p7fxjt7vs.1

Cited by 2 publications

(7 citation statements)

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“…Some works addressed the problem of subcutaneous blood vessel segmentation [ 79 , 80 ] or HFUS image quality assessment [ 55 , 89 ]. A novel trend in CAD algorithms is sharing the datasets or source code [ 67 , 88 , 91 ].…”

Section: Computer-aided Diagnosis Methodsmentioning

confidence: 99%

“…As the authors claimed [ 3 ], it adjusted and scaled the activation, made the segmentation results more stable, and increased the neural network performance. The analyzed dataset [ 67 ] consisted of 380 HFUS images of 380 different patients with inflammatory skin diseases: AD (303) and psoriasis (77). The data were acquired using a DUB SkinnScanner75, tpm (Lueneburg, Germany) [ 30 ] at 75 MHz.…”

Section: Computer-aided Diagnosis Methodsmentioning

confidence: 99%

“… Since the authors did not present the whole datasets or representative samples visualizing the differences in the analyzed images, it was hard to assess the adaptability of the described methods to other types of data. For example, the level-set method described in [ 15 ] when applied to a bigger dataset (380 images [ 67 ]) in [ 3 ] resulted in a median Dice index equal to 0.727 for the epidermis and 0.54 for the SLEB. For the comprehensive dataset analyzed in [ 17 ], which included different diseases, the CNN-based segmentation results were lower than for the inflammatory one.…”

Section: Computer-aided Diagnosis Methodsmentioning

confidence: 99%

“…The two shared datasets are [ 67 , 88 ]. Both are available with a CC BY 4.0 license in the Mendeley Data repository.…”

Section: Data Repositoriesmentioning

confidence: 99%

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

Czajkowska

Borak

2022

Sensors

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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.

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Section: Computer-aided Diagnosis Methodsmentioning

confidence: 99%

Section: Computer-aided Diagnosis Methodsmentioning

confidence: 99%

Section: Computer-aided Diagnosis Methodsmentioning

confidence: 99%

“…The two shared datasets are [ 67 , 88 ]. Both are available with a CC BY 4.0 license in the Mendeley Data repository.…”

Section: Data Repositoriesmentioning

confidence: 99%

See 2 more Smart Citations

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

Czajkowska

Borak

2022

Sensors

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“…To fill this gap, the authors and institutions increasingly publish the data sets through Mendeley Data [28], Center for Artificial Intelligence in Medicine and Imaging [29], GitHub, or other repositories. However, these repositories leave much to be desired for the newest imaging techniques, and only one dataset of HFUS skin images, described in in [30], can be found in Mendeley Data. We collected and shared the face HFUS image database described in this paper to meet this need.…”

Section: Introductionmentioning

confidence: 99%

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

Czajkowska

Juszczyk

Piejko

et al. 2022

Sensors

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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.

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Data for: Deep Learning Approach to Skin Layers Segmentation in Inflammatory Dermatoses

Cited by 2 publications

References 0 publications

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

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

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

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