Department of Automation, Tsinghua University, Beijing, 100084, China.
Analyst. 2024 Mar 25;149(7):2147-2160. doi: 10.1039/d4an00022f.
Droplet microfluidics is a highly sensitive and high-throughput technology extensively utilized in biomedical applications, such as single-cell sequencing and cell screening. However, its performance is highly influenced by the droplet size and single-cell encapsulation rate (following random distribution), thereby creating an urgent need for quality control. Machine learning has the potential to revolutionize droplet microfluidics, but it requires tedious pixel-level annotation for network training. This paper investigates the application software of the weakly supervised cell-counting network (WSCApp) for video recognition of microdroplets. We demonstrated its real-time performance in video processing of microfluidic droplets and further identified the locations of droplets and encapsulated cells. We verified our methods on droplets encapsulating six types of cells/beads, which were collected from various microfluidic structures. Quantitative experimental results showed that our approach can not only accurately distinguish droplet encapsulations (micro-F1 score > 0.94), but also locate each cell without any supervised location information. Furthermore, fine-tuning transfer learning on the pre-trained model also significantly reduced (>80%) annotation. This software provides a user-friendly and assistive annotation platform for the quantitative assessment of cell-encapsulating microfluidic droplets.
液滴微流控技术是一种高度敏感且高通量的技术，广泛应用于生物医学领域，如单细胞测序和细胞筛选。然而，其性能受到液滴大小和单细胞封装率（遵循随机分布）的高度影响，因此需要进行质量控制。机器学习有可能彻底改变液滴微流控技术，但它需要对网络训练进行繁琐的像素级注释。本文研究了弱监督细胞计数网络 (WSCApp) 的应用软件，用于微滴的视频识别。我们展示了它在微流控液滴视频处理中的实时性能，并进一步识别了液滴和封装细胞的位置。我们在从各种微流控结构收集的六种类型的细胞/珠粒封装的液滴上验证了我们的方法。定量实验结果表明，我们的方法不仅可以准确地区分液滴封装（微-F1 分数>0.94），而且无需任何监督位置信息即可定位每个细胞。此外，对预训练模型的迁移学习进行微调也显著减少了（>80%）注释。该软件为定量评估细胞封装微流控液滴提供了一个用户友好且辅助的注释平台。
