A portable Opto-fluidic system for particle separation andquantification using pinched flow fractionation and vision-based object tracking

• Main Authors: Soumyajit Balabantaray, 巴書米
• Other Authors: Chii-Wann Lin
• Format: Others
• Language: en_US
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/9g668w

碩士 === 國立臺灣大學 === 醫學工程學研究所 === 107 === Separation of biomolecules like WBCs (white blood cells), CTCs (circulating tumor cells), polymers from complex samples have extensive clinical significance. For example, WBC count provides implications for the diagnosis and screening of hundreds of diseases and treatments. Microfluidics is the study of systems that can process small quantities of fluids by using tiny channels having dimensions at the microscale. Several microfluidic chip based particle sorting solutions have been provided which manipulate the particle movement inside micro channels to separate them, however many of these techniques require external electrical or magnetic fields, porous membrane filters which raise clogging and fouling effect. If not the above problems, almost every microfluidic device needs bulky expensive pumping system and lab microscopes which limit the use of these valuable microfluidic design solutions in the lab itself. Now the question is, can we find a cost effective and accurate alternative to lab grade microscope and syringe pump to combine with a simple microfluidic design to do cell sorting? A portable opto-fluidic system for particle separation and quantification is proposed which uses a novel microfluidic design called “pinched flow fractionation” along with a smart phone camera and low cost syringe pump to address this issue. It uses vision based particle tracking by defining an identity mapping between corresponding particles of two consecutive frames. An encoder-decoder based convolutional neural network is used to do pixel wise semantic segmentation which generates completely noise free output images essentially required for above identity mapping and further image processing pipeline. Since smart phones are ubiquitous now, this solution provides a possibility for an automated point of care disease diagnosis tool.