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Sci Rep. 2018 Jul 30;8(1):11455. doi: 10.1038/s41598-018-29647-5.

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

Author information

1
Laboratoire Matière et Systèmes Complexes, UMR 7057 CNRS & Université Paris Diderot, 10 rue Alice Domon et Léonie Duquet, 75013, Paris, France. jean-baptiste.lugagne@inria.fr.
2
Inria Saclay - Ile-de-France and Université Paris Saclay, 1 rue Honoré d'Estienne d'Orves, Bâtiment Alan Turing, Campus de l'Ecole Polytechnique, 91120, Palaiseau, France. jean-baptiste.lugagne@inria.fr.
3
Laboratoire Matière et Systèmes Complexes, UMR 7057 CNRS & Université Paris Diderot, 10 rue Alice Domon et Léonie Duquet, 75013, Paris, France.
4
Laboratoire Biologie et Dynamique des Chromosomes, CNRS UMR 7212, Hôpital Saint-Louis, Paris, France.
5
ETH, Swiss Federal Institute of Technology, Zurich, Switzerland.
6
Inria Saclay - Ile-de-France and Université Paris Saclay, 1 rue Honoré d'Estienne d'Orves, Bâtiment Alan Turing, Campus de l'Ecole Polytechnique, 91120, Palaiseau, France.
7
Institut Pasteur and CNRS, C3BI - USR 3756, 25-28 Rue du Docteur Roux, 75015, Paris, France.
8
Laboratoire Matière et Systèmes Complexes, UMR 7057 CNRS & Université Paris Diderot, 10 rue Alice Domon et Léonie Duquet, 75013, Paris, France. pascal.hersen@univ-paris-diderot.fr.

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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