ArticleFrontiers in medicine2020
Automatic Segmentation of Epidermis and Hair Follicles in Optical Coherence Tomography Images of Normal Skin by Convolutional Neural Networks.
Article in Frontiers in medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- A U-Net model for epidermal segmentation in optical coherence tomography images of actinic keratosis.PloS one · 2026Article
- The Laryngovibrogram as a normalized spatiotemporal representation of vocal fold dynamics.Scientific reports · 2025Article
- Quantitative assessment ofBiomedical optics express · 2025Article
- Integrated convolutional neural network for skin cancer classification with hair and noise restoration.Turkish journal of medical sciences · 2025Article
- Segmentation of 3D OCT Images of Human Skin Using Neural Networks with U-Net Architecture.Sovremennye tekhnologii v meditsine · 2025Article
- Precision measurement of stratum corneum thickness in OCT images using variational autoencoders and advanced DSP techniques.Frontiers in bioengineering and biotechnology · 2025Review
- LS-Net: lightweight segmentation network for dermatological epidermal segmentation in optical coherence tomography imaging.Biomedical optics express · 2024Article
- Rapid measurement of epidermal thickness in OCT images of skin.Scientific reports · 2024Article
- Semantic modeling of cell damage prediction: a machine learning approach at human-level performance in dermatology.Scientific reports · 2023Article
- Radiation-induced alterations in multi-layered, in-vitro skin models detected by optical coherence tomography and histological methods.PloS one · 2023Article
- Optical coherence tomography evaluation of vaginal epithelial thickness during COJournal of biophotonics · 2022Article
- Computer-Aided Diagnosis Methods for High-Frequency Ultrasound Data Analysis: A Review.Sensors (Basel, Switzerland) · 2022Review
- Automatic Segmentation of Laser-Induced Injury OCT Images Based on a Deep Neural Network Model.International journal of molecular sciences · 2022Article
- Semisupervised representative learning for measuring epidermal thickness in human subjects in optical coherence tomography by leveraging datasets from rodent models.Journal of biomedical optics · 2022Article
- High-Frequency Ultrasound Dataset for Deep Learning-Based Image Quality Assessment.Sensors (Basel, Switzerland) · 2022Article
- PredictingFrontiers in plant science · 2022Article
- Deep-learning approach for automated thickness measurement of epithelial tissue and scab using optical coherence tomography.Journal of biomedical optics · 2022Article
- The Importance of Mimicking Dermal-Epidermal Junction for Skin Tissue Engineering: A Review.Bioengineering (Basel, Switzerland) · 2021Review
- Deep Learning-Based High-Frequency Ultrasound Skin Image Classification with Multicriteria Model Evaluation.Sensors (Basel, Switzerland) · 2021Article
- Real-time deep learning assisted skin layer delineation in dermal optical coherence tomography.OSA continuum · 2021Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Optical coherence tomography (OCT) is a well-established bedside imaging modality that allows analysis of skin structures in a non-invasive way. Automated OCT analysis of skin layers is of great relevance to study dermatological diseases. In this paper, an approach to detect the epidermal layer along with the follicular structures in healthy human OCT images is presented. To the best of the authors' knowledge, the approach presented in this paper is the only epidermis detection algorithm that segments the pilosebaceous unit, which is of importance in the progression of several skin disorders such as folliculitis, acne, lupus erythematosus, and basal cell carcinoma. The proposed approach is composed of two main stages. The first stage is a Convolutional Neural Network based on U-Net architecture. The second stage is a robust post-processing composed by a Savitzky-Golay filter and Fourier Domain Filtering to fully define the borders belonging to the hair follicles. After validation, an average Dice of 0.83 ± 0.06 and a thickness error of 10.25 μ
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