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Kaltenecker, D. ; Al-Maskari, R. ; Negwer, M. ; Höher, L. ; Kofler, F. ; Zhao, S. ; Todorov, M.I. ; Rong, Z. ; Paetzold, J.C. ; Wiestler, B.* ; Piraud, M. ; Rueckert, D.* ; Geppert, J. ; Morigny, P. ; Rohm, M. ; Menze, B.H.* ; Herzig, S. ; Berriel Diaz, M. ; Ertürk, A.

Virtual reality-empowered deep-learning analysis of brain cells.

Nat. Methods, DOI: 10.1038/s41592-024-02245-2 (2024)
Verlagsversion DOI PMC
Open Access Hybrid
Creative Commons Lizenzvertrag
Automated detection of specific cells in three-dimensional datasets such as whole-brain light-sheet image stacks is challenging. Here, we present DELiVR, a virtual reality-trained deep-learning pipeline for detecting c-Fos+ cells as markers for neuronal activity in cleared mouse brains. Virtual reality annotation substantially accelerated training data generation, enabling DELiVR to outperform state-of-the-art cell-segmenting approaches. Our pipeline is available in a user-friendly Docker container that runs with a standalone Fiji plugin. DELiVR features a comprehensive toolkit for data visualization and can be customized to other cell types of interest, as we did here for microglia somata, using Fiji for dataset-specific training. We applied DELiVR to investigate cancer-related brain activity, unveiling an activation pattern that distinguishes weight-stable cancer from cancers associated with weight loss. Overall, DELiVR is a robust deep-learning tool that does not require advanced coding skills to analyze whole-brain imaging data in health and disease.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Sprache englisch
Veröffentlichungsjahr 2024
HGF-Berichtsjahr 2024
ISSN (print) / ISBN 1548-7091
e-ISSN 1548-7105
Zeitschrift Nature Methods
Verlag Nature Publishing Group
Verlagsort New York, NY
Begutachtungsstatus Peer reviewed
Institut(e) Institute for Tissue Engineering and Regenerative Medicine (ITERM)
Institute of Diabetes and Cancer (IDC)
Helmholtz Artifical Intelligence Cooperation Unit (HAICU)
POF Topic(s) 30205 - Bioengineering and Digital Health
90000 - German Center for Diabetes Research
Forschungsfeld(er) Enabling and Novel Technologies
Helmholtz Diabetes Center
PSP-Element(e) G-505800-001
G-501900-253
G-530001-001
G-501900-257
G-501900-251
Förderungen Institute for Tissue Engineering and Regenerative Medicine
Institute of Molecular Biosciences
Vascular Dementia Research Foundation
Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy within the framework of the Munich Cluster for Systems Neurology
DFG
German Federal Ministry of Education and Research (Bundesministerium fr Bildung und Forschung)
European Research Council Consolidator grant
Nomis Heart Atlas Project Grant (Nomis Foundation)
European Research Council under the European Union
Edith-Haberland-Wagner Stiftung
DFG through TUM International Graduate School of Science and Engineering

Deutsche Forschungsgemeinschaft (German Research Foundation)
DOI 10.1038/s41592-024-02245-2
WOS ID 001206061000001
Scopus ID 85191101827
PubMed ID 38649742
Erfassungsdatum 2024-06-11
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