Nondestructive, quantitative viability analysis of 3D tissue cultures using machine learning image segmentation

arXiv:2311.09354v2 Announce Type: replace-cross
Abstract: Ascertaining the collective viability of cells in different cell culture conditions has typically relied on averaging colorimetric indicators and is often reported out in simple binary readouts. Recent research has combined viability assessment techniques with image-based deep-learning models to automate the characterization of cellular properties. However, further development of viability measurements to assess the continuity of possible cellular states and responses to perturbation across cell culture conditions is needed. In this work, we demonstrate an …

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