Open AccessReal-time deep learning assisted skin layer delineation in dermal optical coherence tomography
Author(s) -
Xuan Li,
Nadiya Chuchvara,
Yuwei Liu,
Babar Rao
Publication year - 2021
Publication title -
osa continuum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.592
H-Index - 8
ISSN - 2578-7519
DOI - 10.1364/osac.426962
Subject(s) - optical coherence tomography , segmentation , margin (machine learning) , computer science , deep learning , artificial intelligence , focus (optics) , biomedical engineering , human skin , medicine , optics , radiology , physics , machine learning , biology , genetics
We present deep learning assisted optical coherence tomography (OCT) imaging for quantitative tissue characterization and differentiation in dermatology. We utilize a manually scanned single fiber OCT (sfOCT) instrument to acquire OCT images from the skin. The focus of this study is to train a U-Net for automatic skin layer delineation. We demonstrate that U-Net allows quantitative assessment of epidermal thickness automatically. U-Net segmentation achieves high accuracy for epidermal thickness estimation for normal skin and leads to a clear differentiation between normal skin and skin lesions. Our results suggest that a single fiber OCT instrument with AI assisted skin delineation capability has the potential to become a cost-effective tool in clinical dermatology, for diagnosis and tumor margin detection.