Hangzhou Dianzi University, School of Automation, Hangzhou, China.
Key Laboratory of Medical Information and 3D Bioprinting of Zhejiang Province, Hangzhou, China.
J Biomed Opt. 2021 Sep;26(9). doi: 10.1117/1.JBO.26.9.095001.
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.
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).
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.
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.
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.
人工皮肤（AS）广泛应用于皮肤病学、药理学和毒理学领域，在移植医学、烧伤创面护理和慢性创面治疗方面具有巨大潜力。人们对高质量的 AS 产品有很大的需求，因此非常希望有一种非侵入性的检测方法。
利用光学相干断层扫描（OCT）定量检测培养周期中 AS 样本的结构参数（即厚度和表面粗糙度），并探索其生长规律。
开发了一种自适应界面检测算法，用于识别每个 A 扫描中的表面点，提供了一种无需构建 OCT B 扫描图像即可快速计算参数的方法，从而实现了 AS 厚度和表面粗糙度的实时定量。在标准粗糙度板和 H&E 染色显微镜上进行了实验验证。
将该方法应用于整个 AS 培养周期，结果表明，在气液培养过程中，皮肤的表面粗糙度先减小后增大，这与显微镜下的角化程度一致。在培养周期中，通过厚度和粗糙度参数可以区分正常和典型异常样本。
自适应界面检测算法适用于具有分层特征组织的高灵敏度、快速检测和定量，可用于非破坏性检测培养周期中 AS 样本厚度和粗糙度的生长规律。
