Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Papillary Dermis01:11

Papillary Dermis

4.7K
Dermis
The dermis might be considered the "core" of the integumentary system, as distinct from the epidermis and hypodermis. It contains blood and lymph vessels, nerves, and other structures, such as hair follicles and sweat glands. The dermis is made of two layers of connective tissue that comprise an interconnected mesh of elastin and collagenous fibers, produced by fibroblasts.
Papillary Layer
The papillary layer is made of loose, areolar connective tissue, which means the collagen...
4.7K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Deep learning based hair removal on ultraviolet-induced fluorescence dermatoscopy images.

Computer methods and programs in biomedicine·2026
Same author

Genetically engineered probiotic E. coli Nissle 1917 enhances protection against Salmonella via increased adhesion and systemic T-cell responses.

NPJ biofilms and microbiomes·2026
Same author

Radiomic feature-based classification of BI-RADS 4/5 breast lesions on contrast-enhanced mammography.

Computer methods and programs in biomedicine·2026
Same author

Actinic keratosis staging in multimodal image data.

Computer methods and programs in biomedicine·2026
Same author

Quantitative Analysis of the Human Face Skin Thickness-A High-Frequency Ultrasound Study.

Journal of clinical medicine·2025
Same author

Corrigendum to "Segmentation of skin layers on HFUS images using the attention mechanism", [Computer Methods and Programs in Biomedicine, 263 (2025) 108668].

Computer methods and programs in biomedicine·2025

Related Experiment Video

Updated: Nov 11, 2025

SCAnED - An Open-source Skin Segmentation Macro for Semi-automated Cell and Nuclei Detection in Epidermal and Dermal Skin Compartments
06:34

SCAnED - An Open-source Skin Segmentation Macro for Semi-automated Cell and Nuclei Detection in Epidermal and Dermal Skin Compartments

Published on: August 8, 2025

288

Deep learning approach to skin layers segmentation in inflammatory dermatoses.

Joanna Czajkowska1, Pawel Badura1, Szymon Korzekwa2

  • 1Faculty of Biomedical Engineering, Silesian University of Technology, Roosevelta 40, 41-800 Zabrze, Poland.

Ultrasonics
|March 30, 2021
PubMed
Summary

This study introduces an automated method for segmenting skin layers using high-frequency ultrasound (HFUS) and deep learning. The AI tool accurately identifies the epidermis and subepidermal low echogenic band, aiding in diagnosing inflammatory skin diseases.

Keywords:
Convolutional neural networkHigh frequency ultrasoundSLEBSegUNetSkin layer segmentation

More Related Videos

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition
09:37

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition

Published on: August 18, 2022

2.6K
Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging
06:08

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

Published on: May 5, 2011

17.0K

Related Experiment Videos

Last Updated: Nov 11, 2025

SCAnED - An Open-source Skin Segmentation Macro for Semi-automated Cell and Nuclei Detection in Epidermal and Dermal Skin Compartments
06:34

SCAnED - An Open-source Skin Segmentation Macro for Semi-automated Cell and Nuclei Detection in Epidermal and Dermal Skin Compartments

Published on: August 8, 2025

288
Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition
09:37

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition

Published on: August 18, 2022

2.6K
Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging
06:08

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

Published on: May 5, 2011

17.0K

Area of Science:

  • Dermatology and Medical Imaging
  • Artificial Intelligence in Healthcare
  • Image Analysis and Segmentation

Background:

  • Monitoring skin layers via medical imaging is crucial for diagnosing and treating chronic inflammatory skin diseases like atopic dermatitis and psoriasis.
  • High-frequency ultrasound (HFUS) enables skin condition monitoring, with epidermis and subepidermal low echogenic band (SLEB) segmentation vital for diagnosis.
  • Manual segmentation by physicians is time-consuming and lacks repeatability, highlighting the need for automated analysis tools in dermatological practice.

Purpose of the Study:

  • To develop an automated method for segmenting epidermis and SLEB layers using HFUS imaging.
  • To improve the accuracy and efficiency of skin layer analysis for diagnosing and monitoring inflammatory skin diseases.
  • To provide a robust tool for assessing treatment effects through precise layer thickness measurements.

Main Methods:

  • Developed an automated segmentation framework combining fuzzy c-means clustering preprocessing with a U-shaped convolutional neural network.
  • The convolutional neural network incorporates batch normalization layers for enhanced segmentation robustness.
  • Performance was evaluated against state-of-the-art methods for skin layer segmentation.

Main Results:

  • The automated method achieved high segmentation accuracy, with Dice coefficients of 0.87 for the epidermis and 0.83 for the SLEB.
  • The developed framework demonstrated superior performance compared to existing state-of-the-art approaches.
  • The results indicate the efficiency and reliability of the automated segmentation tool.

Conclusions:

  • The developed automated segmentation method effectively identifies critical skin layers (epidermis and SLEB) from HFUS images.
  • This AI-driven approach offers a significant improvement over manual segmentation, promising more efficient and repeatable clinical assessments.
  • The framework holds potential for advancing the diagnosis and treatment monitoring of inflammatory skin conditions.