Abstract
The skin is the largest organ of our body. Skin disease abnormalities which occur within the skin layers are difficult to examine visually and often require biopsies to make a confirmation on a suspected condition. Such invasive methods are not well-accepted by children and women due to the possibility of scarring. Optical coherence tomography (OCT) is a non-invasive technique enabling in vivo examination of sub-surface skin tissue without the need for excision of tissue. However, one of the challenges in OCT imaging is the interpretation and analysis of OCT images. In this review, we discuss the various methodologies in skin layer segmentation and how it could potentially improve the management of skin diseases. We also present a review of works which use advanced machine learning techniques to achieve layers segmentation and detection of skin diseases. Lastly, current challenges in analysis and applications are also discussed.
Original language | English |
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Title of host publication | Advances in Experimental Medicine and Biology |
Publisher | Springer |
Pages | 149-163 |
Number of pages | 15 |
DOIs | |
Publication status | Published - 2020 |
Externally published | Yes |
Publication series
Name | Advances in Experimental Medicine and Biology |
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Volume | 1213 |
ISSN (Print) | 0065-2598 |
ISSN (Electronic) | 2214-8019 |
Bibliographical note
Publisher Copyright:© 2020, Springer Nature Switzerland AG.
ASJC Scopus Subject Areas
- General Biochemistry,Genetics and Molecular Biology
Keywords
- Basal cell carcinoma (BCC)
- Deep learning
- Dermal-epidermal junction (DEJ)
- Dermis
- Epidermis
- Graph
- Optical coherence tomography (OCT)
- Roughness
- Segmentation
- Skin