Real-time deep learning assisted skin layer delineation in dermal optical coherence tomography

XUAN LIU, NADIYA CHUCHVARA, YUWEI LIU, BABAR RAO

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

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 languageEnglish (US)
Pages (from-to)2008-2023
Number of pages16
JournalOSA Continuum
Volume4
Issue numberJuly
DOIs
StatePublished - Jul 15 2021

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Electrical and Electronic Engineering

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