Identification of individual cells from z-stacks of bright-field microscopy images

Abstract : Obtaining single cell data from time-lapse microscopy images is critical for quantitative biology, but bottlenecks in cell identification and segmentation must be overcome. We propose a novel, versatile method that uses machine learning classifiers to identify cell morphologies from z-stack bright-field microscopy images. We show that axial information is enough to successfully classify the pixels of an image, without the need to consider in focus morphological features. This fast, robust method can be used to identify different cell morphologies, including the features of E. coli, S. cerevisiae and epithelial cells, even in mixed cultures. Our method demonstrates the potential of acquiring and processing Z-stacks for single-layer, single-cell imaging and segmentation.
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Submitted on : Wednesday, October 17, 2018 - 11:13:23 PM
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Jean-Baptiste Lugagne, Srajan Jain, Pierre Ivanovitch, Zacchary Ben Meriem, Clément Vulin, et al.. Identification of individual cells from z-stacks of bright-field microscopy images. Scientific Reports, Nature Publishing Group, 2018, 8 (1), pp.11455. ⟨10.1038/s41598-018-29647-5⟩. ⟨hal-01898065⟩



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