Deep learning detector for high precision monitoring of cell encapsulation statistics in microfluidic droplets.
Deep learning detector for high precision monitoring of cell encapsulation statistics in microfluidic droplets.
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DOI：
10.1039/d2lc00462c
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发表时间：
2022-10-25
期刊：
影响因子：
6.1
通讯作者：
Li, Wei
中科院分区：
文献类型：
--
作者：
Gardner, Karl;Uddin, Md Mezbah;Linh Tran;Thanh Pham;Vanapalli, Siva;Li, Wei
将细胞封装在微流体液滴内是涉及细胞分析的若干应用的核心。尽管理论上预期包封统计数据遵循泊松分布，但由于缺乏对实验变量和条件的完全控制，在实验上可能无法实现这一点。因此，需要自动检测液滴并对液滴内的细胞计数进行计数，使得这可以用作过程控制反馈以调整实验条件。在这项研究中，我们使用了一种名为You Only Look Once（YOLO）的深度学习对象检测器，这是一种有影响力的对象检测器，与传统方法相比有几个优点。本文研究了YOLOv3和YOLOv5目标探测器在自动化液滴和细胞探测器开发中的应用。实验数据从具有癌细胞分散相的微流体流动聚焦装置获得。微流控装置包含液滴发生器下游的膨胀室，允许可视化和记录细胞包封的液滴图像。在该过程中，预测液滴边界框，然后通过单独的模型从原始图像中裁剪出待检测的单个细胞以进行进一步检查。该系统包括一个生产集，用于使用泊松统计进行额外的性能分析，同时提供液滴和细胞模型的实验工作流程。在标记和应用图像增强之前收集和预处理训练集，从而允许可推广的对象检测器。精确度和召回率被用作验证和测试集度量，导致精确液滴检测器的高平均平均精确度（mAP）度量。为了检查模型的局限性，将预测与地面实况标签进行比较，说明YOLO预测与液滴和细胞标签密切匹配。此外，证明了来自YOLOv5模型的液滴计数与手动计数比率和泊松分布一致，证实了该平台可用于细胞包封优化的实时实验。用于高精度监测微流体液滴中细胞包封统计数据的双模型物体检测系统，并与YOLOv3和YOLOv5性能进行比较。
Encapsulation of cells inside microfluidic droplets is central to several applications involving cellular analysis. Although, theoretically the encapsulation statistics are expected to follow a Poisson distribution, experimentally this may not be achieved due to lack of full control of the experimental variables and conditions. Therefore, there is a need to automatically detect droplets and enumerate cell counts within droplets so that this can be used as process control feedback to adjust experimental conditions. In this study, we use a deep learning object detector called You Only Look Once (YOLO), an influential class of object detectors with several benefits over traditional methods. This paper investigates the application of both YOLOv3 and YOLOv5 object detectors in the development of an automated droplet and cell detector. Experimental data was obtained from a microfluidic flow focusing device with a dispersed phase of cancer cells. The microfluidic device contained an expansion chamber downstream of the droplet generator, allowing for visualization and recording of cell-encapsulated droplet images. In the procedure, a droplet bounding box is predicted, then cropped from the original image for the individual cells to be detected through a separate model for further examination. The system includes a production set for additional performance analysis with Poisson statistics while providing an experimental workflow with both droplet and cell models. The training set is collected and preprocessed before labeling and applying image augmentations, allowing for a generalizable object detector. Precision and recall were utilized as a validation and test set metric, resulting in a high mean average precision (mAP) metric for an accurate droplet detector. To examine model limitations, the predictions were compared to ground truth labels, illustrating that the YOLO predictions closely matched with the droplet and cell labels. Furthermore, it is demonstrated that droplet enumeration from the YOLOv5 model is consistent with hand counted ratios and the Poisson distribution, confirming that the platform can be used in real-time experiments for cell encapsulation optimization. A dual model object detection system for high precision monitoring of cell encapsulation statistics in microfluidic droplets with comparisons from YOLOv3 and YOLOv5 performance.
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