| |  Online-Ressource |
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| Verfasst von: | Deininger, Luca [VerfasserIn]  |
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| | Jung-Klawitter, Sabine [VerfasserIn] |
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| | Mikut, Ralf [VerfasserIn] |
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| | Richter, Petra [VerfasserIn] |
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| | Fischer, Manuel [VerfasserIn] |
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| | Karimian-Jazi, Kianush [VerfasserIn] |
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| | Breckwoldt, Michael O. [VerfasserIn] |
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| | Bendszus, Martin [VerfasserIn] |
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| | Heiland, Sabine [VerfasserIn] |
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| | Kleesiek, Jens Philipp [VerfasserIn] |
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| | Opladen, Thomas [VerfasserIn] |
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| | Kuseyri Hübschmann, Oya [VerfasserIn] |
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| | Hübschmann, Daniel [VerfasserIn] |
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| | Schwarz, Daniel [VerfasserIn] |
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| Titel: | An AI-based segmentation and analysis pipeline for high-field MR monitoring of cerebral organoids |
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| Verf.angabe: | Luca Deininger, Sabine Jung-Klawitter, Ralf Mikut, Petra Richter, Manuel Fischer, Kianush Karimian-Jazi, Michael O. Breckwoldt, Martin Bendszus, Sabine Heiland, Jens Kleesiek, Thomas Opladen, Oya Kuseyri Hübschmann, Daniel Hübschmann, Daniel Schwarz |
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| E-Jahr: | 2023 |
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| Jahr: | 01 December 2023 |
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| Umfang: | 9 S. |
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| Illustrationen: | Illustrationen |
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| Fussnoten: | Gesehen am 06.05.2024 |
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| Titel Quelle: | Enthalten in: Scientific reports |
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| Ort Quelle: | [London] : Macmillan Publishers Limited, part of Springer Nature, 2011 |
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| Jahr Quelle: | 2023 |
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| Band/Heft Quelle: | 13(2023), Artikel-ID 21231, Seite 1-9 |
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| ISSN Quelle: | 2045-2322 |
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| Abstract: | Cerebral organoids recapitulate the structure and function of the developing human brain in vitro, offering a large potential for personalized therapeutic strategies. The enormous growth of this research area over the past decade with its capability for clinical translation makes a non-invasive, automated analysis pipeline of organoids highly desirable. This work presents a novel non-invasive approach to monitor and analyze cerebral organoids over time using high-field magnetic resonance imaging and state-of-the-art tools for automated image analysis. Three specific objectives are addressed, (I) organoid segmentation to investigate organoid development over time, (II) global cysticity classification and (III) local cyst segmentation for organoid quality assessment. We show that organoid growth can be monitored reliably over time and cystic and non-cystic organoids can be separated with high accuracy, with on par or better performance compared to state-of-the-art tools applied to brightfield imaging. Local cyst segmentation is feasible but could be further improved in the future. Overall, these results highlight the potential of the pipeline for clinical application to larger-scale comparative organoid analysis. |
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| DOI: | doi:10.1038/s41598-023-48343-7 |
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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/10.1038/s41598-023-48343-7 |
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| | kostenfrei: Volltext: https://www.nature.com/articles/s41598-023-48343-7 |
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| | DOI: https://doi.org/10.1038/s41598-023-48343-7 |
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| Datenträger: | Online-Ressource |
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| Sprache: | eng |
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| Sach-SW: | Machine learning |
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| | Magnetic resonance imaging |
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| | Stem cells |
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| K10plus-PPN: | 1887881085 |
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| Verknüpfungen: | → Zeitschrift |
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¬An¬ AI-based segmentation and analysis pipeline for high-field MR monitoring of cerebral organoids / Deininger, Luca [VerfasserIn]; 01 December 2023 (Online-Ressource)
