Label-free cell counting and viability prediction with brightfield imaging and deep learning
Abstract
Cell viability assessment is a core requirement in cell culture systems, with critical applications in biopharmaceutical manufacturing and drug development. Conventionally, it is measured by adding membrane-impermeable dyes to a sample (a process called staining), which allows compromised cell membranes to be distinguished from intact ones. 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. 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. We then report the development and validation of ViabiLens, an AI-assisted software tool for label-free cell viability analysis. 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. Evaluated on Chinese Hamster Ovary (CHO) cells spanning a wide range of viability conditions, ViabiLens achieves a mean absolute error of 2.33\% on unstained samples against fluorescence-based reference measurements. The demo of ViabiLens software is available at https://amirrezavazifeh.github.io/ViabiLens-Project-Page/.
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BibTeX
@misc{vazifeh2026labelfree,
title = {{Label-free cell counting and viability prediction with brightfield imaging and deep learning}},
author = {Amir Reza Vazifeh and Christian Zeigler and Sornanathan Meyyappan and Richard Jeske and Jason W. Fleischer},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.10473},
eprint = {2610.10473},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.10473},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Label-free cell counting and viability prediction with brightfield imaging and deep learning
AU - Amir Reza Vazifeh
AU - Christian Zeigler
AU - Sornanathan Meyyappan
AU - Richard Jeske
AU - Jason W. Fleischer
PY - 2026
DA - 2026/10/27
PB - arXiv
DO - 10.48550/arxiv.2610.10473
UR - https://arxiv.org/abs/2610.10473
AB - Cell viability assessment is a core requirement in cell culture systems, with critical applications in biopharmaceutical manufacturing and drug development. Conventionally, it is measured by adding membrane-impermeable dyes to a sample (a process called staining), which allows compromised cell membranes to be distinguished from intact ones. 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. 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. We then report the development and validation of ViabiLens, an AI-assisted software tool for label-free cell viability analysis. 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. Evaluated on Chinese Hamster Ovary (CHO) cells spanning a wide range of viability conditions, ViabiLens achieves a mean absolute error of 2.33\% on unstained samples against fluorescence-based reference measurements. The demo of ViabiLens software is available at https://amirrezavazifeh.github.io/ViabiLens-Project-Page/.
ER -