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| Verfasst von: | Vijayan, Athul [VerfasserIn]  |
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| | Mody, Tejasvinee Atul [VerfasserIn] |
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| | Yu, Qin [VerfasserIn] |
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| | Wolny, Adrian [VerfasserIn] |
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| | Cerrone, Lorenzo [VerfasserIn] |
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| | Strauss, Soeren [VerfasserIn] |
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| | Tsiantis, Miltos [VerfasserIn] |
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| | Smith, Richard S. [VerfasserIn] |
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| | Hamprecht, Fred [VerfasserIn] |
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| | Kreshuk, Anna [VerfasserIn] |
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| | Schneitz, Kay [VerfasserIn] |
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| Titel: | A deep learning-based toolkit for 3D nuclei segmentation and quantitative analysis in cellular and tissue context |
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| Verf.angabe: | Athul Vijayan, Tejasvinee Atul Mody, Qin Yu, Adrian Wolny, Lorenzo Cerrone, Soeren Strauss, Miltos Tsiantis, Richard S. Smith, Fred A. Hamprecht, Anna Kreshuk and Kay Schneitz |
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| E-Jahr: | 2024 |
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| Jahr: | 18 July 2024 |
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| Umfang: | 15 S. |
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| Fussnoten: | Gesehen am 24.02.2024 |
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| Titel Quelle: | Enthalten in: Development |
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| Ort Quelle: | Cambridge : The Company of Biologists, 1953 |
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| Jahr Quelle: | 2024 |
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| Band/Heft Quelle: | 151(2024), 14, Artikel-ID dev2028004, Seite 1-15 |
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| ISSN Quelle: | 1477-9129 |
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| Abstract: | We present a new set of computational tools that enable accurate and widely applicable 3D segmentation of nuclei in various 3D digital organs. We have developed an approach for ground truth generation and iterative training of 3D nuclear segmentation models, which we applied to popular CellPose, PlantSeg and StarDist algorithms. We provide two high-quality models trained on plant nuclei that enable 3D segmentation of nuclei in datasets obtained from fixed or live samples, acquired from different plant and animal tissues, and stained with various nuclear stains or fluorescent protein-based nuclear reporters. We also share a diverse high-quality training dataset of about 10,000 nuclei. Furthermore, we advanced the MorphoGraphX analysis and visualization software by, among other things, providing a method for linking 3D segmented nuclei to their surrounding cells in 3D digital organs. We found that the nuclear-to-cell volume ratio varies between different ovule tissues and during the development of a tissue. Finally, we extended the PlantSeg 3D segmentation pipeline with a proofreading tool that uses 3D segmented nuclei as seeds to correct cell segmentation errors in difficult-to-segment tissues. |
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| DOI: | doi:10.1242/dev.202800 |
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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.
kostenfrei: Volltext: https://doi.org/10.1242/dev.202800 |
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| | DOI: https://doi.org/10.1242/dev.202800 |
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| Datenträger: | Online-Ressource |
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| Sprache: | eng |
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| K10plus-PPN: | 1917906226 |
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| Verknüpfungen: | → Zeitschrift |
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¬A¬ deep learning-based toolkit for 3D nuclei segmentation and quantitative analysis in cellular and tissue context / Vijayan, Athul [VerfasserIn]; 18 July 2024 (Online-Ressource)
