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, vi...反射式共聚焦显微镜（Reflectance Confocal Microscopy, RCM）是一种可用于多种皮肤疾病活体检测的高性能工具。然而，当前RCM的应用仍依赖人工专家对皮肤各解剖层级中的特异性特征开展定性评估。若要开发量化RCM影像特征的方法，需实现对给定直面断层截面中所含解剖皮肤分层的自动识别。本研究提出一种自动化分析方案：采用词袋特征（Bag of Features）方法表征直面断层截面，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（viable epidermis）、真皮表皮交界处（dermal-epidermal junction）与乳头层真皮（papillary dermis）。该方案基于来自54名志愿者的308组深度堆叠图像数据集完成开发与测试，志愿者被分为两个年龄组（20~30岁与50~70岁）。测试集上的分类准确率达85.6%。在测定角质层/活表皮、vi...