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Pré-Publication, Document De Travail Année : 2021

Automated Reconstruction of Whole-Embryo Cell Lineages by Learning from Sparse Annotations

Caroline Malin-Mayor
  • Fonction : Auteur
Peter Hirsch
  • Fonction : Auteur
Katie Mcdole
  • Fonction : Auteur
Yinan Wan
  • Fonction : Auteur
William Lemon
  • Fonction : Auteur
Philipp Keller
  • Fonction : Auteur
Stephan Preibisch
  • Fonction : Auteur
Jan Funke
  • Fonction : Auteur

Résumé

Abstract We present a method for automated nucleus identification and tracking in time-lapse microscopy recordings of entire developing embryos. Our method combines deep learning and global optimization to enable complete lineage reconstruction from sparse point annotations, and uses parallelization to process multi-terabyte light-sheet recordings, which we demonstrate on three common model organisms: mouse, zebrafish, Drosophila . On the most difficult dataset (mouse), our method correctly reconstructs 75.8% of cell lineages spanning 1 hour, compared to 31.8% for the previous state of the art, thus enabling biologists to determine where and when cell fate decisions are made in developing embryos, tissues, and organs.

Dates et versions

hal-03320776 , version 1 (16-08-2021)

Identifiants

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Caroline Malin-Mayor, Peter Hirsch, Léo Guignard, Katie Mcdole, Yinan Wan, et al.. Automated Reconstruction of Whole-Embryo Cell Lineages by Learning from Sparse Annotations. 2021. ⟨hal-03320776⟩
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