Significance: Artificial skin (AS) is widely used in dermatology, pharmacology, and toxicology, and has great potential in transplant medicine, burn wound care, and chronic wound treatment. There is a great demand for high-quality AS product and a non-invasive detection method is highly desirable. Aim: To quantify the constructure parameters (i.e., thickness and surface roughness) of AS samples in the culture cycle and explore the growth regularities using optical coherent tomography (OCT). Approach: An adaptive interface detection algorithm is developed to recognize surface points in each A-scan, offering a rapid method to calculate parameters without constructing OCT B-scan pictures and further achieving realizing real-time quantification of AS thickness and surface roughness. Experiments on standard roughness plates and H&E-staining microscopy were performed as a verification. Results: As applied on the whole cycle of AS culture, our method’s results show that during the air–liquid culture, the surface roughness of the skin first decreases and then exhibits an increase, which implies coincidence with the degree of keratinization under a microscope. And normal and typical abnormal samples can be differentiated by thickness and roughness parameters during the culture cycle. Conclusions: The adaptive interface detection algorithm is suitable for high-sensitivity, fast detection, and quantification of the interface with layered characteristic tissues, and can be used for non-destructive detection of the growth regularity of AS sample thickness and roughness during the culture cycle.

中文翻译：

---

基于光学相干断层扫描的人造皮肤生长周期结构参数自动定量分析

意义：人造皮肤（AS）广泛应用于皮肤病学、药理学和毒理学，在移植医学、烧伤创面护理、慢性创面治疗等方面具有巨大潜力。对高质量 AS 产品的需求很大，非常需要一种非侵入性的检测方法。目的：量化培养周期中 AS 样品的结构参数（即厚度和表面粗糙度），并使用光学相干断层扫描 (OCT) 探索生长规律。方法：开发自适应界面检测算法，识别每次A-scan中的表面点，提供一种无需构建OCT B-scan图片即可快速计算参数的方法，进一步实现对AS厚度和表面粗糙度的实时量化。标准粗糙度板和H&的实验 进行电子染色显微镜检查作为验证。结果：应用于整个AS培养周期，我们的方法结果表明，在气液培养过程中，皮肤表面粗糙度先降低后增加，这与显微镜下的角质化程度相符。并且可以通过培养周期中的厚度和粗糙度参数区分正常和典型的异常样品。结论：自适应界面检测算法适用于具有分层特征组织的界面的高灵敏度、快速检测和量化，可用于培养周期内AS样品厚度和粗糙度的生长规律的无损检测。 . 应用于整个AS培养周期，我们的方法结果表明，在气液培养过程中，皮肤表面粗糙度先下降后增加，这与显微镜下的角化程度相符。并且可以通过培养周期中的厚度和粗糙度参数区分正常和典型的异常样品。结论：自适应界面检测算法适用于具有分层特征组织的界面的高灵敏度、快速检测和量化，可用于培养周期内AS样品厚度和粗糙度的生长规律的无损检测。 . 应用于整个AS培养周期，我们的方法结果表明，在气液培养过程中，皮肤表面粗糙度先下降后增加，这与显微镜下的角化程度相符。并且可以通过培养周期中的厚度和粗糙度参数区分正常和典型的异常样品。结论：自适应界面检测算法适用于具有分层特征组织的界面的高灵敏度、快速检测和量化，可用于培养周期内AS样品厚度和粗糙度的生长规律的无损检测。 . 这意味着与显微镜下的角化程度相符。并且可以通过培养周期中的厚度和粗糙度参数区分正常和典型的异常样品。结论：自适应界面检测算法适用于具有分层特征组织的界面的高灵敏度、快速检测和量化，可用于培养周期内AS样品厚度和粗糙度的生长规律的无损检测。 . 这意味着与显微镜下的角化程度相符。并且可以通过培养周期中的厚度和粗糙度参数区分正常和典型的异常样品。结论：自适应界面检测算法适用于具有分层特征组织的界面的高灵敏度、快速检测和量化，可用于培养周期内AS样品厚度和粗糙度的生长规律的无损检测。 .