Deep learning‐enabled imaging flow cytometry for high‐speed Cryptosporidium and Giardia detection. Issue 11 (20th February 2021)

• Record Type: Journal Article
• Authors: Luo, Shaobo; Nguyen, Kim Truc; Nguyen, Binh T. T.; Feng, Shilun; Shi, Yuzhi; Elsayed, Ahmed; Zhang, Yi; Zhou, Xiaohong; Wen, Bihan; Chierchia, Giovanni; Talbot, Hugues; Bourouina, Tarik; Jiang, Xudong; Liu, Ai Qun
• Other Names: Su Xuantao guestEditor.
• Is Part Of: Cytometry. Volume 99:Issue 11(2021)
• Page Start: 1123
• Page End: 1133
• Publication Date: 2021-02-20
• Subjects: cell classification -- convolutional neural network -- deep learning -- imaging flow cytometry; Flow cytometry -- Periodicals; Imaging systems in biology -- Periodicals; Imaging systems in medicine -- Periodicals; Diagnostic imaging -- Periodicals; 571.605
• DOI: 10.1002/cyto.a.24321
• Languages: English
• ISSNs: 1552-4922

Abstract

Abstract: 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.