Deep learning enabled label-free microfluidic droplet classification for single cell functional assays.

ABSTRACT: Droplet-based microfluidics techniques coupled to microscopy allow for the characterization of cells at the single-cell scale. However, such techniques generate substantial amounts of data and microscopy images that must be analyzed. Droplets on these images usually need to be classified depending on the number of cells they contain. This verification, when visually carried out by the experimenter image-per-image, is time-consuming and impractical for analysis of many assays or when an assay yields many putative droplets of interest. Machine learning models have already been developed to classify cell-containing droplets within microscopy images, but not in the context of assays in which non-cellular structures are present inside the droplet in addition to cells. Here we develop a deep lea

SUBMITTER: Vanhoucke T 

PROVIDER: S-EPMC11445169 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature