An AI-based segmentation and analysis pipeline for high-field MR monitoring of cerebral organoids
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-inva...
| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-12-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-48343-7 |
| _version_ | 1827399527376617472 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Luca Deininger |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T19:47:21Z |
| format | Article |
| id | doaj.art-5feda2c05aa64bae999a541da954ed8b |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-08T19:47:21Z |
| publishDate | 2023-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-5feda2c05aa64bae999a541da954ed8b2023-12-24T12:16:48ZengNature PortfolioScientific Reports2045-23222023-12-011311910.1038/s41598-023-48343-7An AI-based segmentation and analysis pipeline for high-field MR monitoring of cerebral organoidsLuca Deininger0Sabine Jung-Klawitter1Ralf Mikut2Petra Richter3Manuel Fischer4Kianush Karimian-Jazi5Michael O. Breckwoldt6Martin Bendszus7Sabine Heiland8Jens Kleesiek9Thomas Opladen10Oya Kuseyri Hübschmann11Daniel Hübschmann12Daniel Schwarz13Group for Automated Image and Data Analysis, Institute for Automation and Applied Informatics, Karlsruhe Institute of TechnologyDivision of Pediatric Neurology and Metabolic Medicine, Department I, Center for Pediatric and Adolescent Medicine, Medical Faculty Heidelberg, Heidelberg UniversityGroup for Automated Image and Data Analysis, Institute for Automation and Applied Informatics, Karlsruhe Institute of TechnologyDivision of Pediatric Neurology and Metabolic Medicine, Department I, Center for Pediatric and Adolescent Medicine, Medical Faculty Heidelberg, Heidelberg UniversityDepartment of Neuroradiology, Heidelberg University HospitalDepartment of Neuroradiology, Heidelberg University HospitalDepartment of Neuroradiology, Heidelberg University HospitalDepartment of Neuroradiology, Heidelberg University HospitalDepartment of Neuroradiology, Heidelberg University HospitalInstitute for Artificial Intelligence in Medicine (IKIM), University Hospital EssenDivision of Pediatric Neurology and Metabolic Medicine, Department I, Center for Pediatric and Adolescent Medicine, Medical Faculty Heidelberg, Heidelberg UniversityDivision of Pediatric Neurology and Metabolic Medicine, Department I, Center for Pediatric and Adolescent Medicine, Medical Faculty Heidelberg, Heidelberg UniversityGerman Cancer Consortium (DKTK)Department of Neuroradiology, Heidelberg University HospitalAbstract 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.https://doi.org/10.1038/s41598-023-48343-7 |
| spellingShingle | 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 An AI-based segmentation and analysis pipeline for high-field MR monitoring of cerebral organoids Scientific Reports |
| 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 |
| url | https://doi.org/10.1038/s41598-023-48343-7 |
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