Gioe Eric, Uddin Mohammed Raihan, Kim Jong-Hoon, Chen Xiaolin
School of Engineering and Computer Science, Washington State University Vancouver, 14204 NE Salmon Creek Ave, Vancouver, WA 98686, USA.
Micromachines (Basel). 2022 Apr 23;13(5):661. doi: 10.3390/mi13050661.
Deterministic lateral displacement (DLD) is a microfluidic method for the continuous separation of particles based on their size. There is growing interest in using DLD for harvesting circulating tumor cells from blood for further assays due to its low cost and robustness. While DLD is a powerful tool and development of high-throughput DLD separation devices holds great promise in cancer diagnostics and therapeutics, much of the experimental data analysis in DLD research still relies on error-prone and time-consuming manual processes. There is a strong need to automate data analysis in microfluidic devices to reduce human errors and the manual processing time. In this work, a reliable particle detection method is developed as the basis for the DLD separation analysis. Python and its available packages are used for machine vision techniques, along with existing identification methods and machine learning models. Three machine learning techniques are implemented and compared in the determination of the DLD separation mode. The program provides a significant reduction in video analysis time in DLD separation, achieving an overall particle detection accuracy of 97.86% with an average computation time of 25.274 s.
确定性侧向位移（DLD）是一种基于颗粒大小对其进行连续分离的微流控方法。由于其低成本和稳健性，人们越来越有兴趣使用DLD从血液中捕获循环肿瘤细胞以进行进一步检测。虽然DLD是一种强大的工具，并且高通量DLD分离装置的开发在癌症诊断和治疗方面具有巨大潜力，但DLD研究中的许多实验数据分析仍然依赖于容易出错且耗时的手动过程。迫切需要实现微流控装置中的数据分析自动化，以减少人为错误和手动处理时间。在这项工作中，开发了一种可靠的颗粒检测方法作为DLD分离分析的基础。Python及其可用包用于机器视觉技术，同时结合现有的识别方法和机器学习模型。在确定DLD分离模式时，实施并比较了三种机器学习技术。该程序显著减少了DLD分离中的视频分析时间，实现了97.86%的整体颗粒检测准确率，平均计算时间为25.274秒。
