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.

中文翻译：

---

使用卷积神经网络从 OCT 图像测量指尖皮肤表皮厚度

在这项研究中，我们提出了一种基于深度卷积神经网络的皮肤表皮厚度（ET）测量方法，该方法用于确定皮肤表面和真皮-表皮交界处（DEJ）的脊部边界。指尖皮肤的剖面光学相干断层扫描 (OCT) 图像。ET是根据地表与脊顶的行差计算的，该差是通过搜索脊部分边界的局部最大值来确定的。结果表明，DEJ中脊部区域确定良好，本工作中的ET测量可以将DEJ中乳突谷的影响降低9.85%。可用于皮肤的定量表征，以区分皮肤病。