Long-term tracking of budding yeast cells ...
Type de document :
Compte-rendu et recension critique d'ouvrage
DOI :
Titre :
Long-term tracking of budding yeast cells in brightfield microscopy: CellStar and the Evaluation Platform
Auteur(s) :
Versari, Cristian [Auteur]
BioComputing
Stoma, Szymon [Auteur]
Scientific Center for Optical and Electron Microscopy [ScopeM]
Batmanov, Kirill [Auteur]
BioComputing
Llamosi, Artémis [Auteur]
Computational systems biology and optimization [Lifeware]
Matière et Systèmes Complexes [MSC]
Mroz, Filip [Auteur]
Wroclaw University of Science and Technology
Kaczmarek, Adam [Auteur]
Wroclaw University of Science and Technology
Deyell, Matt [Auteur]
Matière et Systèmes Complexes [MSC]
Lhoussaine, Cedric [Auteur]
BioComputing
Hersen, Pascal [Auteur]
Matière et Systèmes Complexes [MSC]
Batt, Gregory [Auteur]
Computational systems biology and optimization [Lifeware]
BioComputing
Stoma, Szymon [Auteur]
Scientific Center for Optical and Electron Microscopy [ScopeM]
Batmanov, Kirill [Auteur]
BioComputing
Llamosi, Artémis [Auteur]
Computational systems biology and optimization [Lifeware]
Matière et Systèmes Complexes [MSC]
Mroz, Filip [Auteur]
Wroclaw University of Science and Technology
Kaczmarek, Adam [Auteur]
Wroclaw University of Science and Technology
Deyell, Matt [Auteur]
Matière et Systèmes Complexes [MSC]
Lhoussaine, Cedric [Auteur]
BioComputing
Hersen, Pascal [Auteur]
Matière et Systèmes Complexes [MSC]
Batt, Gregory [Auteur]
Computational systems biology and optimization [Lifeware]
Titre de la revue :
Journal of the Royal Society Interface
Pagination :
32
Éditeur :
the Royal Society
Date de publication :
2017
ISSN :
1742-5689
Mot(s)-clé(s) en anglais :
computational biology
image analysis
segmentation and tracking
parameter learning
imaging benchmark
image analysis
segmentation and tracking
parameter learning
imaging benchmark
Discipline(s) HAL :
Informatique [cs]/Bio-informatique [q-bio.QM]
Résumé en anglais : [en]
With the continuous expansion of single cell biology, the observation of the behaviour of individual cells over extended durations and with high accuracy has become a problem of central importance. Surprisingly, even for ...
Lire la suite >With the continuous expansion of single cell biology, the observation of the behaviour of individual cells over extended durations and with high accuracy has become a problem of central importance. Surprisingly, even for yeast cells that have relatively regular shapes, no solution has been proposed that reaches the high quality required for long-term experiments for segmentation and tracking (S&T) based on brightfield images. Here, we present CellStar, a tool chain designed to achieve good performance in long-term experiments. The key features are the use of a new variant of parametrized active rays for seg-mentation, a neighbourhood-preserving criterion for tracking, and the use of an iterative approach that incrementally improves S&T quality. A graphical user interface enables manual corrections of S&T errors and their use for the automated correction of other, related errors and for parameter learning. We created a benchmark dataset with manually analysed images and compared CellStar with six other tools, showing its high performance, notably in long-term tracking. As a community effort, we set up a website, the Yeast Image Toolkit, with the benchmark and the Evaluation Platform to gather this and additional information provided by others.Lire moins >
Lire la suite >With the continuous expansion of single cell biology, the observation of the behaviour of individual cells over extended durations and with high accuracy has become a problem of central importance. Surprisingly, even for yeast cells that have relatively regular shapes, no solution has been proposed that reaches the high quality required for long-term experiments for segmentation and tracking (S&T) based on brightfield images. Here, we present CellStar, a tool chain designed to achieve good performance in long-term experiments. The key features are the use of a new variant of parametrized active rays for seg-mentation, a neighbourhood-preserving criterion for tracking, and the use of an iterative approach that incrementally improves S&T quality. A graphical user interface enables manual corrections of S&T errors and their use for the automated correction of other, related errors and for parameter learning. We created a benchmark dataset with manually analysed images and compared CellStar with six other tools, showing its high performance, notably in long-term tracking. As a community effort, we set up a website, the Yeast Image Toolkit, with the benchmark and the Evaluation Platform to gather this and additional information provided by others.Lire moins >
Langue :
Anglais
Vulgarisation :
Non
Projet ANR :
Des modèles de population aux populations de modèles: observation, modélisation et contrôle de l'expression génique au niveau de la cellule unique
Modèles à effets mixtes de processus intracellulaires: méthodes, outils et applications
Contrôle automatisé de l'expression des gènes
Université Sorbonne Paris Cité
Modèles à effets mixtes de processus intracellulaires: méthodes, outils et applications
Contrôle automatisé de l'expression des gènes
Université Sorbonne Paris Cité
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