Label-free cell counting and viability prediction with brightfield imaging and deep learning

Key points

• Cell viability assessment is a core requirement in cell culture systems, with critical applications in biopharmaceutical manufacturing and drug development.1
• However, staining has several limitations: (a) chemical agents can perturb normal cellular processes of the cells being measured, (b) it is often ambiguous to assign viability to individual cells whose membrane integrity is only partially compromised. (c) photobleaching can undermine measurement accuracy over time when using fluorescent stains, and (d) staining cannot be performed in situ or in real time.1
• We then report the development and validation of ViabiLens, an AI-assisted software for label-free cell viability analysis.1
• The ViabiLens combines a cell detection model for localizing individual cells with a convolutional neural network (CNN) classifier for live/dead prediction, paired with an interactive UMAP-based viewer for visualizing and exploring individual cells across the sample.1

Sources (1)

• [1]Label-free cell counting and viability prediction with brightfield imaging and deep learning
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 7, 05:34 PM

  Here, we show that (1) stained cells captured under brightfield imaging contain sufficient information to distinguish live and dead cells, and (2) cells captured under unstained brightfield imaging exhibit similar image features to their stained counterparts, enabling models trained on stained cells to generalize to unstained ones.

  Cell viability assessment is a core requirement in cell culture systems, with critical applications in biopharmaceutical manufacturing and drug development.

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