Deep-Learning Based Label-Free Classification of Activated and Inactivated Neutrophils for Rapid Immune State Monitoring.

Journal: Sensors (Basel, Switzerland)
Published Date: Jan 13, 2021

Abstract

The differential count of white blood cells (WBCs) is one widely used approach to assess the status of a patient's immune system. Currently, the main methods of differential WBC counting are manual counting and automatic instrument analysis with labeling preprocessing. But these two methods are complicated to operate and may interfere with the physiological states of cells. Therefore, we propose a deep learning-based method to perform label-free classification of three types of WBCs based on their morphologies to judge the activated or inactivated neutrophils. Over 90% accuracy was finally achieved by a pre-trained fine-tuning Resnet-50 network. This deep learning-based method for label-free WBC classification can tackle the problem of complex instrumental operation and interference of fluorescent labeling to the physiological states of the cells, which is promising for future point-of-care applications.

Authors

  • Xiwei Huang
    Ministry of Education Key Lab of RF Circuits and Systems, Hangzhou Dianzi University, Hangzhou 310018, China. huangxiwei@hdu.edu.cn.
  • Hyungkook Jeon
    Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
  • Jixuan Liu
    Key Laboratory of RF Circuits and Systems, Ministry of Education, Hangzhou Dianzi University, Hangzhou 310018, China.
  • Jiangfan Yao
    Key Laboratory of RF Circuits and Systems, Ministry of Education, Hangzhou Dianzi University, Hangzhou 310018, China.
  • Maoyu Wei
    Key Laboratory of RF Circuits and Systems, Ministry of Education, Hangzhou Dianzi University, Hangzhou 310018, China.
  • Wentao Han
    College of Biological Sciences and Biotechnology, Beijing Forestry University Beijing, China.
  • Jin Chen
    Department of Neurology, University of Texas Health Science Center at Houston, Houston, TX.
  • Lingling Sun
    Key Laboratory of RF Circuits and Systems, Ministry of Education, Hangzhou Dianzi University, Hangzhou 310018, China; Zhejiang Provincial Laboratory of Integrated Circuits Design, Hangzhou Dianzi University, Hangzhou 310018, China.
  • Jongyoon Han
    Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.

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