O'Dwyer Kevin, Domijan Katarina, Dignam Adam, Butler Marion, Hennelly Bryan M
Department of Electronic Engineering, Maynooth University, Maynooth, Ireland.
Department of Mathematics and Statistics, Maynooth University, Maynooth, Ireland.
Cancers (Basel). 2021 Sep 24;13(19):4767. doi: 10.3390/cancers13194767.
Raman micro-spectroscopy is a powerful technique for the identification and classification of cancer cells and tissues. In recent years, the application of Raman spectroscopy to detect bladder, cervical, and oral cytological samples has been reported to have an accuracy greater than that of standard pathology. However, despite being entirely non-invasive and relatively inexpensive, the slow recording time, and lack of reproducibility have prevented the clinical adoption of the technology. Here, we present an automated Raman cytology system that can facilitate high-throughput screening and improve reproducibility. The proposed system is designed to be integrated directly into the standard pathology clinic, taking into account their methodologies and consumables. The system employs image processing algorithms and integrated hardware/software architectures in order to achieve automation and is tested using the ThinPrep standard, including the use of glass slides, and a number of bladder cancer cell lines. The entire automation process is implemented, using the open source Micro-Manager platform and is made freely available. We believe that this code can be readily integrated into existing commercial Raman micro-spectrometers.
拉曼显微光谱是一种用于识别和分类癌细胞及组织的强大技术。近年来，有报道称拉曼光谱在检测膀胱、宫颈和口腔细胞学样本方面的应用，其准确性高于标准病理学。然而，尽管该技术完全无创且相对廉价，但记录时间长以及缺乏可重复性阻碍了其在临床上的应用。在此，我们展示了一种自动化拉曼细胞学系统，该系统能够促进高通量筛查并提高可重复性。所提出的系统设计为可直接集成到标准病理诊所中，同时考虑到其方法和耗材。该系统采用图像处理算法以及集成的硬件/软件架构以实现自动化，并使用包括载玻片在内的ThinPrep标准以及多种膀胱癌细胞系进行测试。整个自动化过程是使用开源的Micro-Manager平台实现的，并且可以免费获取。我们相信这段代码能够很容易地集成到现有的商用拉曼光谱仪中。
