微流控液滴中基于机器学习的微珠计数提高了单细胞分拣应用中监测微珠封装的可靠性

IF 2.3 4区 工程技术 Q2 INSTRUMENTS & INSTRUMENTATION
Hoang Anh Phan, Nguyen Dang Pham, Loc Quang Do, Tung Thanh Bui, Hai Hoang Nguyen, Trinh Duc Chu
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引用次数: 0

摘要

将细胞包裹在液滴中是各种细胞分析应用的一个重要方面。目前的研究主要集中在利用明视野图像中的目标检测功能准确检测和识别液滴内的细胞类型或细胞数量。然而,对于从光学系统获取的图像数据质量对计算机视觉模型的影响,目前只有少数几项深入研究。本研究考察了几种流行的机器学习对象检测模型,分析了液滴内珠子位置识别复杂化的情况,这对计算机视觉模型提出了挑战。研究人员开发并实施了一种微流控液滴生成系统,并结合光学设备捕捉液滴内封装珠子的图像。为了确定最有效的模型,从整体数据中精心挑选了一个特定的数据集,其中包括描绘珠子重叠和边缘漂移情况的图像。所提出的方法在测试数据集上达到了 98.2% 的准确率,在使用 YOLOv8 模型进行实时测试时达到了 95% 的准确率,提高了液滴内珠子计数的精确度,并明确了准确率与帧识别阈值之间的相关性。这项工作对单细胞分拣具有特别重要的意义,因为单细胞分拣的精确度对确保获得有意义的结果至关重要,尤其是对癌细胞等稀有细胞类型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Machine learning-based bead enumeration in microfluidics droplets enhances the reliability of monitoring bead encapsulation toward single-cell sorting applications

Machine learning-based bead enumeration in microfluidics droplets enhances the reliability of monitoring bead encapsulation toward single-cell sorting applications

The encapsulation of cells within droplets is a crucial aspect of various cell analysis applications. Current research has focused on accurately detecting and identifying cell types or cell counts within droplets using object detection in bright-field images. However, there are only a few in-depth investigations into the impact of the image data quality acquired from optical systems on computer vision models. This study examines several popular machine learning object detection models to analyze scenarios complicating the identification of bead locations within a droplet, posing challenges for computer vision models. A microfluidic droplet generation system was developed and implemented, coupled with optical devices to capture images of encapsulated beads within the droplet. To identify the most efficient model, a specific dataset was meticulously selected from the overall data, encompassing images depicting overlapping beads and edge-drifting scenarios. The proposed method achieved up to 98.2% accuracy on the testing dataset and 95% in real-time testing with the YOLOv8 model, enhancing bead count precision within droplets and clarifying the correlation between accuracy and frame recognition thresholds. This work holds particular importance in single-cell sorting, where precision is critical in ensuring meaningful outcomes, particularly concerning rare cell types such as cancer cells.

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来源期刊
Microfluidics and Nanofluidics
Microfluidics and Nanofluidics 工程技术-纳米科技
CiteScore
4.80
自引率
3.60%
发文量
97
审稿时长
2 months
期刊介绍: Microfluidics and Nanofluidics is an international peer-reviewed journal that aims to publish papers in all aspects of microfluidics, nanofluidics and lab-on-a-chip science and technology. The objectives of the journal are to (1) provide an overview of the current state of the research and development in microfluidics, nanofluidics and lab-on-a-chip devices, (2) improve the fundamental understanding of microfluidic and nanofluidic phenomena, and (3) discuss applications of microfluidics, nanofluidics and lab-on-a-chip devices. Topics covered in this journal include: 1.000 Fundamental principles of micro- and nanoscale phenomena like, flow, mass transport and reactions 3.000 Theoretical models and numerical simulation with experimental and/or analytical proof 4.000 Novel measurement & characterization technologies 5.000 Devices (actuators and sensors) 6.000 New unit-operations for dedicated microfluidic platforms 7.000 Lab-on-a-Chip applications 8.000 Microfabrication technologies and materials Please note, Microfluidics and Nanofluidics does not publish manuscripts studying pure microscale heat transfer since there are many journals that cover this field of research (Journal of Heat Transfer, Journal of Heat and Mass Transfer, Journal of Heat and Fluid Flow, etc.).
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