Long-term tracking of budding yeast cells in brightfield microscopy: CellStar and the Evaluation Platform
| Autor: | Versari, Cristian, Stoma, Szymon, Batmanov, Kirill, Llamosi, Artémis, Mroz, Filip, Kaczmarek, Adam, Deyell, Matt, Lhoussaine, Cédric, Hersen, Pascal, Batt, Gregory |
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| Přispěvatelé: | BioComputing, Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS), Scientific Center for Optical and Electron Microscopy (ScopeM), Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] (ETH Zürich), Computational systems biology and optimization (Lifeware), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Matière et Systèmes Complexes (MSC), Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), Wroclaw University of Science and Technology, ANR-10-BINF-0006,Iceberg,Des modèles de population aux populations de modèles: observation, modélisation et contrôle de l'expression génique au niveau de la cellule unique(2010), ANR-16-CE33-0018,MEMIP,Modèles à effets mixtes de processus intracellulaires: méthodes, outils et applications(2016), ANR-16-CE12-0025,COGEX,Contrôle automatisé de l'expression des gènes(2016), ANR-11-IDEX-0005,USPC,Université Sorbonne Paris Cité(2011), Matière et Systèmes Complexes (MSC (UMR_7057)), Centre National de la Recherche Scientifique (CNRS)-Université Paris Diderot - Paris 7 (UPD7), Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189 (CRIStAL), Centre National de la Recherche Scientifique (CNRS)-Université de Lille-Ecole Centrale de Lille-Centre National de la Recherche Scientifique (CNRS)-Université de Lille-Ecole Centrale de Lille, Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology in Zürich [Zürich] (ETH Zürich), Wroclaw University of Technology [Wroclaw], ANR-10-BINF-06-11,Iceberg,Des modèles de population aux populations de modèles: observation, modélisation et contrôle de l’expression génique au niveau de la cellule unique(2011), ANR-11-IDEX-0005-02/11-LABX-0071,WHO AM I,Determinants de l’Identité : de la molécule à l’individu(2011) |
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
imaging benchmark
Image analysis Segmentation and tracking Parameter learning Imaging benchmark segmentation and tracking parameter learning computational biology Cell Tracking image analysis Schizosaccharomyces Image Processing Computer-Assisted [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM] Life Sciences–Engineering interface Research Article |
| Zdroj: | Journal of the Royal Society Interface Journal of the Royal Society Interface, 2017, 14 (127), pp.32. ⟨10.1098/rsif.2016.0705⟩ Journal of the Royal Society Interface, the Royal Society, 2017, 14 (127), pp.32. ⟨10.1098/rsif.2016.0705⟩ Journal of the Royal Society. Interface, 14 (127) |
| ISSN: | 1742-5689 1742-5662 |
| Popis: | 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 segmentation, 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. ISSN:1742-5689 ISSN:1742-5662 |
| Databáze: | OpenAIRE |
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