Machine Learning Based Single-Frame Super-Resolution Processing for Lensless Blood Cell Counting.

Xiwei Huang,Mei Yan,Hailong Rong,Zhi Han,Yu Jiang,Hao Yu,Hang Xu,Haiping Yang,Xu Liu

doi:10.3390/s16111836

Abstract

A lensless blood cell counting system integrating microfluidic channel and a complementary metal oxide semiconductor (CMOS) image sensor is a promising technique to miniaturize the conventional optical lens based imaging system for point-of-care testing (POCT). However, such a system has limited resolution, making it imperative to improve resolution from the system-level using super-resolution (SR) processing. Yet, how to improve resolution towards better cell detection and recognition with low cost of processing resources and without degrading system throughput is still a challenge. In this article, two machine learning based single-frame SR processing types are proposed and compared for lensless blood cell counting, namely the Extreme Learning Machine based SR (ELMSR) and Convolutional Neural Network based SR (CNNSR). Moreover, lensless blood cell counting prototypes using commercial CMOS image sensors and custom designed backside-illuminated CMOS image sensors are demonstrated with ELMSR and CNNSR. When one captured low-resolution lensless cell image is input, an improved high-resolution cell image will be output. The experimental results show that the cell resolution is improved by 4×, and CNNSR has 9.5% improvement over the ELMSR on resolution enhancing performance. The cell counting results also match well with a commercial flow cytometer. Such ELMSR and CNNSR therefore have the potential for efficient resolution improvement in lensless blood cell counting systems towards POCT applications.

Highlights

Blood cell counts in point-of-care testing (POCT) provide critical information for rapid on-site disease diagnosis and monitoring [1,2]
The counts of red blood cells (RBC, erythrocytes), white blood cell (WBC, leukocytes), and platelets help the diagnosis of anemia; the CD4+ lymphocyte count is used to monitor the progression of HIV/AIDS [3]
Convolutional Neural Network based SR (CNNSR) has 9.5% improvement over the Extreme Learning Machine based SR (ELMSR)

Summary

Introduction

Blood cell counts in point-of-care testing (POCT) provide critical information for rapid on-site disease diagnosis and monitoring [1,2]. Existing techniques for blood cell counting mainly include manual counting using high magnification optical microscopy with high–numerical aperture objective lenses, or automated counting using commercial flow cytometers. Sensors 2016, 16, 1836 experiences, whereas commercial flow cytometers with bulky and sophisticated optics are prohibitively Sensors 2016, 16, 1836 expensive. Both are not suitable for POCT applications. With the recent development of microfluidic lab-on-a-chip and mass sophisticated optics are prohibitively expensive

Methods

Results

Conclusion