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.
|Original language||English (US)|
|Number of pages||16|
|State||Published - Jul 15 2021|
All Science Journal Classification (ASJC) codes
- Electronic, Optical and Magnetic Materials
- Atomic and Molecular Physics, and Optics
- Electrical and Electronic Engineering