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首页> 外文期刊>Frontiers in Medicine >Automatic Segmentation of Epidermis and Hair Follicles in Optical Coherence Tomography Images of Normal Skin by Convolutional Neural Networks
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Automatic Segmentation of Epidermis and Hair Follicles in Optical Coherence Tomography Images of Normal Skin by Convolutional Neural Networks

机译：卷积神经网络普通皮肤光相干断层扫描图像中表皮和毛囊的自动分割

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摘要

Optical coherence tomography (OCT) is a well-established bedside imaging modality that allows analysis of skin structures in a non-invasive way. Automated OCT analysis of skin layers is of great relevance to study dermatological diseases. In this paper, an approach to detect the epidermal layer along with the follicular structures in healthy human OCT images is presented. To the best of the authors' knowledge, the approach presented in this paper is the only epidermis detection algorithm that segments the pilosebaceous unit, which is of importance in the progression of several skin disorders such as folliculitis, acne, lupus erythematosus, and basal cell carcinoma. The proposed approach is composed of two main stages. The first stage is a Convolutional Neural Network based on U-Net architecture. The second stage is a robust post-processing composed by a Savitzky-Golay filter and Fourier Domain Filtering to fully define the borders belonging to the hair follicles. After validation, an average Dice of 0.83 ± 0.06 and a thickness error of 10.25 μ m is obtained on 270 human skin OCT images. Based on these results, the proposed method outperforms other state-of-the-art methods for epidermis segmentation. It demonstrates that the proposed image segmentation method successfully detects the epidermal region in a fully automatic way in addition to defining the follicular skin structures as main novelty.
机译：光学相干断层扫描（OCT）是一种良好的床头旁观成像模型，允许以非侵入性方式分析皮肤结构。自动化OCT对皮肤层的分析与研究皮肤病有很大的相关性。在本文中，提出了一种检测表皮层以及健康人OCT图像中的滤色结构的方法。据作者所知，本文呈现的方法是唯一的表皮检测算法，其分段是皮脂糖基因单元，这在毛囊炎，痤疮，狼疮红斑和基底细胞等几种皮肤病的进展中是重要的癌。所提出的方法由两个主要阶段组成。第一阶段是基于U-Net架构的卷积神经网络。第二阶段是由Savitzky-Golay滤波器和傅立叶域滤波组成的强大后处理，以完全定义属于毛卵泡的边界。验证后，在270人皮肤OCT图像上获得0.83±0.06的平均骰子和10.25μm的厚度误差。基于这些结果，所提出的方法优于表皮细分的其他最先进的方法。除了将滤泡性皮肤结构定义为主要新颖性之外，所提出的图像分割方法还以全自动的方式成功地检测表皮区域。

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