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Accurate and Robust Trypan Blue-Based Cell Viability Measurement Using Neural Networks



Adele Peskin, Steven Lund, Chenyi Ling, Laura Pierce, Sumona Sarkar, Firdavs Kurbanov, Michael Halter, Joe Chalfoun, John T. Elliott


Trypan blue dye exclusion-based cell viability measurements are highly dependent upon image quality and consistency. In order to make measurements repeatable, one must be able to reliably capture images at a consistent focal plane, and with signal-to-noise ratio within appropriate limits to support proper execution of image analysis routines. Using neural networks to determine whether cells in a bright field image are dead or alive has done away with the need to return to the sharpest image with each new sample in order to take a high-quality measurement. The algorithm is no longer trying to find human-specified image features. Instead, neural networks use convolutional layers to detect the relevant feature information to detect and classify single cells as live or dead. Our viability measurements can be made over a wide range of focal planes (up to 150 µm), and viability levels (0-80 % viability in test sets), while keeping the viability estimates to within that made using manual labeling.
Proceedings Title
CVPR 2021 open access
Conference Dates
June 19-25, 2021
Conference Location
Virtual, CO, US
Conference Title
Computer Vision for Microscopy Image Analysis (CVMI)


cell viability, neural networks


Peskin, A. , Lund, S. , Ling, C. , Pierce, L. , Sarkar, S. , Kurbanov, F. , Halter, M. , Chalfoun, J. and Elliott, J. (2021), Accurate and Robust Trypan Blue-Based Cell Viability Measurement Using Neural Networks, CVPR 2021 open access, Virtual, CO, US, [online], (Accessed May 25, 2024)


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Created July 19, 2021, Updated May 5, 2023