Development of A Label-Free Imaging Droplet Sorting System with Machine Learning-Support Vector Machine (SVM)

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题名:	Development of A Label-Free Imaging Droplet Sorting System with Machine Learning-Support Vector Machine (SVM)
作者:	黃正毅;Huang, Cheng-Yi
贡献者:	機械工程學系
关键词:	液滴分選;機器學習;液滴;晶片實驗室;Droplet sorting;Machine learning;Droplet based microfluidic;Lab-on-a-chip
日期:	2018-01-24
上传时间:	2018-04-13 11:29:39 (UTC+8)
出版者:	國立中央大學
摘要:	隨著電腦科學的進步基於影像分析的液滴分選系統隨之發展，影像分析是最直觀的觀測手段，藉由圖像的輪廓、大小、形狀、透光度等性質的差異，即可判斷出是否含有目標，且不須添加任何的額外物質，這有助於生物方面的研究，讓細胞在最不受干擾的環境下可以被單獨的觀察。為了實現這個技術，本論文藉助了一套SVM機器學習的影像分類系統，嘗試開發一套不需標記的影像分選系統。在此系統中，液滴形成並自然的包封7微米的微珠，接著在液滴流動過程中再通入連續相溶液控制液滴的間距以符合系統的要求，最後由分選系統將含有微珠的液滴挑選出來。為了更進一步的研究這個系統，本論文將液滴分選系統分為三階段討論，第一階段目標為形成穩定的液滴並觀測在不同的三個壓力參數下所得的液滴大小、生成頻率、間距等關鍵參數，最後由實驗我們推導出可用於第三階段的壓力組合區域。第二階段則是觀察7微米微珠的包封狀況，在實驗中我們驗證其物理原理並由實驗獲得一估算方式 (eq. 4.2.4)，估算含有不同數量微珠的液滴的出現機率，這有助於得到單顆粒的液滴。而第三部分我們結合影像分析系統，對於SVM機器學習的影像分析系統，我們首先使用10000顆液滴來訓練並驗證分類模型的準確度，若學習完成則結合微流晶片並觀測其分選準確度，由實驗我們確信我們的系統已具有初步判斷液滴是否含有微珠的功能，並達到每秒鐘約50顆液滴的分選速率以及95%的系統準確度和100%分選準確度。此外我們也即將完成多類分選模型的訓練，目前其系統準確率依所使用的分類模式已達97.5% 和97.73%。 ;As the computer science progressing, image processing based droplet sorting system has been reported. Image processing is the most direct method of observation. It can detect target by target contour, size, shape, transmittance, etc. It is helpful for biological research because cells can be separated without any treatment. In this thesis, we integrate an image classification system with (support vector machine) SVM machine learning and an electric droplet sorter to try to develop a new droplet sorting technology, label-free image sorting system. In this system, droplets are formed and naturally encapsulate micro particles. Then, continuous fluid is injected to fit system requirement by controlling droplet condition like droplet separation. Finally, the system sort droplets with particles out. To further study this system, we divide the droplet sorting system into 3 stages. In the first stage, we aim to stably generate monodisperse droplets and observe some critical parameters like droplet generation frequency, and droplet separation. From this experiment, we get the pressure combination that can be used in the third stage, droplet sorting system. The second stage is to observe the encapsulation of 7 μm particles. In the experiment, we verify its physical principle and get an estimate equation (eq. 4.2.4). This equation estimates the probability of occurrence of droplets containing different numbers of particles and help for getting droplet with single particle. In the third stage, we study a droplet sorting system. For SVM machine learning image detection system, we first train this classification model and verify its accuracy. If its learning process is successful, the system is integrated to sorter and test system accuracy. From our experiment, we believe our system already has ability to identify droplet with or without particles and classify at a rate of 50 droplets per second. We also find the system accuracy and sorting accuracy are up to 95% and 100%, respectively. In addition, we are training the multi-classification model to find out single particle droplets. The system accuracy has reached 97.5% and 97.73% depends on strategy.
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