A measurement of epidermal thickness of fingertip skin from OCT images using convolutional neural network

被引：10
作者：
Lin, Yongping [1 ]
Li, Dezi [2 ]
Liu, Wang [2 ]
Zhong, Zhaowei [2 ]
Li, Zhifang [2 ]
He, Youwu [2 ]
Wu, Shulian [2 ]
机构：
[1] Xiamen Univ Technol, Sch Optoelect & Commun Engn, Fujian Prov Key Lab Optoelect Technol & Devices, Xiamen 361024, Peoples R China
[2] Fujian Normal Univ, Minist Educ,Fujian Prov Key Lab Photon Technol, Coll Photon & Elect Engn,Fujian Prov Engn Technol, Key Lab Optoelect Sci & Technol Med, Fuzhou 350007, Fujian, Peoples R China
来源：
JOURNAL OF INNOVATIVE OPTICAL HEALTH SCIENCES | 2021年 / 14卷 / 01期
基金：
美国国家科学基金会; 中国国家自然科学基金;
关键词：
Epidermal thickness; cross-section OCT images; convolutional neural network; OPTICAL COHERENCE TOMOGRAPHY; SEGMENTATION; MICROSCOPY; JUNCTION; 3D;
D O I：
10.1142/S1793545821400058
中图分类号：
O43 [光学];
学科分类号：
070207 ; 0803 ;
摘要：
In this study, we proposed a method to measure the epidermal thickness (ET) of skin based on deep convolutional neural network, which was used to determine the boundaries of skin surface and the ridge portion in dermal-epidermis junction (DEJ) in cross-section optical coherence tomography (OCT) images of fingertip skin. The ET was calculated based on the row difference between the surface and the ridge top, which is determined by search the local maxima of boundary of the ridge portion. The results demonstrated that the region of ridge portion in DEJ was well determined and the ET measurement in this work can reduce the effect of the papillae valley in DEJ by 9.85%. It can be used for quantitative characterization of skin to differentiate the skin diseases.
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页数：7
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