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

Document Type

Article

Publication Date

7-15-2021

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.

Identifier

85110975597 (Scopus)

Publication Title

OSA Continuum

External Full Text Location

https://doi.org/10.1364/OSAC.426962

e-ISSN

25787519

First Page

2008

Last Page

2023

Issue

July

Volume

4

Grant

1R15CA213092-01A1

Fund Ref

National Institutes of Health

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