ESIEE, Universite Paris-Est, Noisy-le-Grand Cedex, France.
Nanyang Environment and Water Research Institute, Nanyang Technological University, Singapore, Singapore.
Cytometry A. 2021 Nov;99(11):1123-1133. doi: 10.1002/cyto.a.24321. Epub 2021 Feb 20.
Imaging flow cytometry has become a popular technology for bioparticle image analysis because of its capability of capturing thousands of images per second. Nevertheless, the vast number of images generated by imaging flow cytometry imposes great challenges for data analysis especially when the species have similar morphologies. In this work, we report a deep learning-enabled high-throughput system for predicting Cryptosporidium and Giardia in drinking water. This system combines imaging flow cytometry and an efficient artificial neural network called MCellNet, which achieves a classification accuracy >99.6%. The system can detect Cryptosporidium and Giardia with a sensitivity of 97.37% and a specificity of 99.95%. The high-speed analysis reaches 346 frames per second, outperforming the state-of-the-art deep learning algorithm MobileNetV2 in speed (251 frames per second) with a comparable classification accuracy. The reported system empowers rapid, accurate, and high throughput bioparticle detection in clinical diagnostics, environmental monitoring and other potential biosensing applications.
成像流式细胞术因其每秒可捕获数千张图像的能力，已成为生物粒子图像分析的热门技术。然而，成像流式细胞术产生的大量图像给数据分析带来了巨大的挑战，特别是当物种具有相似的形态时。在这项工作中，我们报告了一种基于深度学习的高通量系统，用于预测饮用水中的隐孢子虫和贾第鞭毛虫。该系统结合了成像流式细胞术和一种名为 MCellNet 的高效人工神经网络，实现了>99.6%的分类准确率。该系统对隐孢子虫和贾第鞭毛虫的检测灵敏度为 97.37%，特异性为 99.95%。高速分析速度达到 346 帧/秒，优于最先进的深度学习算法 MobileNetV2 的速度（251 帧/秒），具有相当的分类准确率。所报道的系统能够在临床诊断、环境监测和其他潜在生物传感应用中实现快速、准确和高通量的生物粒子检测。
