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An AI-based segmentation and analysis pipeline for high-field MR monitoring of cerebral organoids
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, aut...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10692072/ https://www.ncbi.nlm.nih.gov/pubmed/38040865 http://dx.doi.org/10.1038/s41598-023-48343-7 |
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author | Deininger, Luca Jung-Klawitter, Sabine Mikut, Ralf Richter, Petra Fischer, Manuel Karimian-Jazi, Kianush Breckwoldt, Michael O. Bendszus, Martin Heiland, Sabine Kleesiek, Jens Opladen, Thomas Hübschmann, Oya Kuseyri Hübschmann, Daniel Schwarz, Daniel |
author_facet | Deininger, Luca Jung-Klawitter, Sabine Mikut, Ralf Richter, Petra Fischer, Manuel Karimian-Jazi, Kianush Breckwoldt, Michael O. Bendszus, Martin Heiland, Sabine Kleesiek, Jens Opladen, Thomas Hübschmann, Oya Kuseyri Hübschmann, Daniel Schwarz, Daniel |
author_sort | Deininger, Luca |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10692072 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-106920722023-12-03 An AI-based segmentation and analysis pipeline for high-field MR monitoring of cerebral organoids Deininger, Luca Jung-Klawitter, Sabine Mikut, Ralf Richter, Petra Fischer, Manuel Karimian-Jazi, Kianush Breckwoldt, Michael O. Bendszus, Martin Heiland, Sabine Kleesiek, Jens Opladen, Thomas Hübschmann, Oya Kuseyri Hübschmann, Daniel Schwarz, Daniel Sci Rep Article 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. Nature Publishing Group UK 2023-12-01 /pmc/articles/PMC10692072/ /pubmed/38040865 http://dx.doi.org/10.1038/s41598-023-48343-7 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Deininger, Luca Jung-Klawitter, Sabine Mikut, Ralf Richter, Petra Fischer, Manuel Karimian-Jazi, Kianush Breckwoldt, Michael O. Bendszus, Martin Heiland, Sabine Kleesiek, Jens Opladen, Thomas Hübschmann, Oya Kuseyri Hübschmann, Daniel Schwarz, Daniel An AI-based segmentation and analysis pipeline for high-field MR monitoring of cerebral organoids |
title | An AI-based segmentation and analysis pipeline for high-field MR monitoring of cerebral organoids |
title_full | An AI-based segmentation and analysis pipeline for high-field MR monitoring of cerebral organoids |
title_fullStr | An AI-based segmentation and analysis pipeline for high-field MR monitoring of cerebral organoids |
title_full_unstemmed | An AI-based segmentation and analysis pipeline for high-field MR monitoring of cerebral organoids |
title_short | An AI-based segmentation and analysis pipeline for high-field MR monitoring of cerebral organoids |
title_sort | ai-based segmentation and analysis pipeline for high-field mr monitoring of cerebral organoids |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10692072/ https://www.ncbi.nlm.nih.gov/pubmed/38040865 http://dx.doi.org/10.1038/s41598-023-48343-7 |
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