Automated Blood Cell Detection and Counting via Deep Learning for Microfluidic Point-of-Care Medical Devices

Tiancheng Xia,Yong Qing Fu,Richard Jiang,Nanlin Jin

doi:10.1088/1757-899x/646/1/012048

Abstract

Automated in-vitro cell detection and counting have been a key theme for artificial and intelligent biological analysis such as biopsy, drug analysis and decease diagnosis. Along with the rapid development of microfluidics and lab-on-chip technologies, in-vitro live cell analysis has been one of the critical tasks for both research and industry communities. However, it is a great challenge to obtain and then predict the precise information of live cells from numerous microscopic videos and images. In this paper, we investigated in-vitro detection of white blood cells using deep neural networks, and discussed how state-of-the-art machine learning techniques could fulfil the needs of medical diagnosis. The approach we used in this study was based on Faster Region-based Convolutional Neural Networks (Faster RCNNs), and a transfer learning process was applied to apply this technique to the microscopic detection of blood cells. Our experimental results demonstrated that fast and efficient analysis of blood cells via automated microscopic imaging can achieve much better accuracy and faster speed than the conventionally applied methods, implying a promising future of this technology to be applied to the microfluidic point-of-care medical devices.

Highlights

Microfluidic technologies [1, 2] have recently found wide-range applications in biological and medical applications, such as lab-on-chip and point-of-care (POC) diagnostic devices, which revolutionised the personalized medicine and rapid disease diagnosis
In this work, aiming at the integration of automated microscopic image analysis into microfluidic POC device for blood cell counting, we investigated the methods of live cell detection techniques based on a recently developed artificial intelligence (AI) approach
Experimental Results After our White blood cell (WBC) detector was trained on the small train dataset, we run the test on the rest of images, e.g., 314 test images

Summary

Introduction

Microfluidic technologies [1, 2] have recently found wide-range applications in biological and medical applications, such as lab-on-chip and point-of-care (POC) diagnostic devices, which revolutionised the personalized medicine and rapid disease diagnosis. This contrasts with the conventional treatment, in which testing was wholly or mostly confined to the medical laboratory In this case, the specimens are often taken away from the point of care and hours even days will be waited for the results, during which the point of care is asked to wait before the critical information is obtained. The specimens are often taken away from the point of care and hours even days will be waited for the results, during which the point of care is asked to wait before the critical information is obtained Such the POC diagnosis has facilitated a paradigm shift from therapeutic treatments to predictive, personalized and preventive ones [1, 2], and from the conventional diagnostic tests performed inside the clinical labs settings to near-patient ones.

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