Data from: Automated segmentation of skin strata in reflectance confocal microscopy depth stacks

Reflectance confocal microscopy (RCM) is a powerful tool for in-vivo examination of a variety of skin diseases. However, current use of RCM depends on qualitative examination by a human expert to look for specific features in the different strata of the skin. Developing approaches to quantify features in RCM imagery requires an automated understanding of what anatomical strata is present in a given en-face section. This work presents an automated approach using a bag of features approach to represent en-face sections and a logistic regression classifier to classify sections into one of four classes (stratum corneum, viable epidermis, dermal-epidermal junction and papillary dermis). This approach was developed and tested using a dataset of 308 depth stacks from 54 volunteers in two age groups (20–30 and 50–70 years of age). The classification accuracy on the test set was 85.6%. The mean absolute error in determining the interface depth for each of the stratum corneum/viable epidermis, viable epidermis/dermal-epidermal junction and dermal-epidermal junction/papillary dermis interfaces were 3.1 μm, 6.0 μm and 5.5 μm respectively. The probabilities predicted by the classifier in the test set showed that the classifier learned an effective model of the anatomy of human skin.

反射共聚焦显微镜（Reflectance confocal microscopy, RCM）是一种可用于多种皮肤病活体检测的功能强大的工具。然而，当前RCM的应用仍依赖于人工专家对皮肤各层的特定特征进行定性检视。开发量化RCM图像特征的方法，需要自动识别给定正面截面中存在的皮肤解剖层级。本研究提出一种自动化分析方法：采用词袋特征表示法（bag of features approach）表征正面截面，并通过逻辑回归分类器将截面划分为四类，分别为角质层（stratum corneum）、活表皮层（viable epidermis）、真皮表皮交界区（dermal-epidermal junction）以及真皮乳头层（papillary dermis）。该方法依托54名志愿者的308组深度堆栈数据集完成开发与测试，志愿者被划分为20~30岁与50~70岁两个年龄组。测试集上的分类准确率达85.6%。针对角质层/活表皮层、活表皮层/真皮表皮交界区以及真皮表皮交界区/真皮乳头层这三个界面的深度测定，其平均绝对误差分别为3.1 μm、6.0 μm与5.5 μm。分类器在测试集上得到的预测概率显示，该分类器已学习到有效的人体皮肤解剖结构模型。

搜集汇总

背景与挑战

背景概述

该数据集包含反射共聚焦显微镜（RCM）深度堆栈的皮肤层次自动分割数据，用于量化皮肤不同层次的特征。数据集包括308个深度堆栈，来自54名志愿者，分为两个年龄组，分类准确率达到85.6%。数据文件包括不同分辨率的图像和元数据文件。