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Dynamic video recognition for cell-encapsulating microfluidic droplets.
- Source :
-
Analyst . 4/7/2024, Vol. 149 Issue 7, p2147-2160. 14p. - Publication Year :
- 2024
-
Abstract
- 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00032654
- Volume :
- 149
- Issue :
- 7
- Database :
- Academic Search Index
- Journal :
- Analyst
- Publication Type :
- Academic Journal
- Accession number :
- 176219359
- Full Text :
- https://doi.org/10.1039/d4an00022f