Imaging sebaceous gland using optical coherence tomography with deep learning assisted automatic identification |
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| Authors: | Yuemei Luo Xianghong Wang Xiaojun Yu Ruibing Jin Linbo Liu |
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| Institution: | 1. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore;2. School of Automation, Northwestern Polytechnical University, Xi'an, China;3. Institute for Infocomm Research, Agency for Science, Technology and Research (A*STAR), Singapore, Singapore |
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| Abstract: | Imaging sebaceous glands and evaluating morphometric parameters are important for diagnosis and treatment of serum problems. In this article, we investigate the feasibility of high-resolution optical coherence tomography (OCT) in combination with deep learning assisted automatic identification for these purposes. Specifically, with a spatial resolution of 2.3 μm × 6.2 μm (axial × lateral, in air), OCT is capable of clearly differentiating sebaceous gland from other skin structures and resolving the sebocyte layer. In order to achieve efficient and timely imaging analysis, a deep learning approach built upon ResNet18 is developed to automatically classify OCT images (with/without sebaceous gland), with a classification accuracy of 97.9%. Based on the result of automatic identification, we further demonstrate the possibility to measure gland size, sebocyte layer thickness and gland density. |
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| Keywords: | computer-aided diagnosis deep learning optical coherence tomography optical imaging sebaceous glands |
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