| |  Online-Ressource |
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| Verfasst von: | Wolny, Adrian [VerfasserIn]  |
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| | Cerrone, Lorenzo [VerfasserIn] |
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| | Vijayan, Athul [VerfasserIn] |
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| | Tofanelli, Rachele [VerfasserIn] |
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| | Vilches-Barro, Amaya [VerfasserIn] |
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| | Louveaux, Marion [VerfasserIn] |
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| | Wenzl, Christian [VerfasserIn] |
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| | Strauss, Sören [VerfasserIn] |
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| | Wilson-Sánchez, David [VerfasserIn] |
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| | Lymbouridou, Rena [VerfasserIn] |
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| | Steigleder, Susanne S. [VerfasserIn] |
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| | Pape, Constantin [VerfasserIn] |
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| | Bailoni, Alberto [VerfasserIn] |
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| | Duran-Nebreda, Salva [VerfasserIn] |
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| | Bassel, George W [VerfasserIn] |
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| | Lohmann, Jan U. [VerfasserIn] |
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| | Tsiantis, Miltos [VerfasserIn] |
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| | Hamprecht, Fred [VerfasserIn] |
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| | Schneitz, Kay [VerfasserIn] |
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| | Maizel, Alexis [VerfasserIn] |
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| | Kreshuk, Anna [VerfasserIn] |
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| Titel: | Accurate and versatile 3D segmentation of plant tissues at cellular resolution |
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| Verf.angabe: | Adrian Wolny, Lorenzo Cerrone, Athul Vijayan, Rachele Tofanelli, Amaya Vilches Barro, Marion Louveaux, Christian Wenzl, Sören Strauss, David Wilson-Sánchez, Rena Lymbouridou, Susanne S Steigleder, Constantin Pape, Alberto Bailoni, Salva Duran-Nebreda, George W Bassel, Jan U Lohmann, Miltos Tsiantis, Fred A Hamprecht, Kay Schneitz, Alexis Maizel, Anna Kreshuk |
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| E-Jahr: | 2020 |
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| Jahr: | 29 July 2020 |
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| Umfang: | 35 S. |
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| Fussnoten: | Gesehen am 26.10.2020 |
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| Titel Quelle: | Enthalten in: eLife |
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| Ort Quelle: | Cambridge : eLife Sciences Publications, 2012 |
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| Jahr Quelle: | 2020 |
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| Band/Heft Quelle: | 9(2020) Artikel-Nummer e57613, 35 Seiten |
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| ISSN Quelle: | 2050-084X |
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| Abstract: | Quantitative analysis of plant and animal morphogenesis requires accurate segmentation of individual cells in volumetric images of growing organs. In the last years, deep learning has provided robust automated algorithms that approach human performance, with applications to bio-image analysis now starting to emerge. Here, we present PlantSeg, a pipeline for volumetric segmentation of plant tissues into cells. PlantSeg employs a convolutional neural network to predict cell boundaries and graph partitioning to segment cells based on the neural network predictions. PlantSeg was trained on fixed and live plant organs imaged with confocal and light sheet microscopes. PlantSeg delivers accurate results and generalizes well across different tissues, scales, acquisition settings even on non plant samples. We present results of PlantSeg applications in diverse developmental contexts. PlantSeg is free and open-source, with both a command line and a user-friendly graphical interface. |
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| DOI: | doi:10.7554/eLife.57613 |
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| URL: | Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.
Volltext: https://doi.org/10.7554/eLife.57613 |
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| | DOI: https://doi.org/10.7554/eLife.57613 |
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| Datenträger: | Online-Ressource |
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| Sprache: | eng |
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| Sach-SW: | cell segmentation |
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| | deep learning |
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| | image analysis |
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| | instance segmentation |
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| K10plus-PPN: | 1736548786 |
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| Verknüpfungen: | → Zeitschrift |
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Accurate and versatile 3D segmentation of plant tissues at cellular resolution / Wolny, Adrian [VerfasserIn]; 29 July 2020 (Online-Ressource)
