Virtual reality-empowered deep-learning analysis of brain cells
Open accessPublished version of a preprint
Abstract
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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Tools for comprehensive, high-resolution analysis of disease-associated changes at the whole-body scale have been lacking.
“However, tools for comprehensive, high-resolution analysis of disease-associated changes at the whole-body scale have been lacking.”
From A deep-learning framework reveals whole-body perturbations at cell level (2026) · cites this paper as [7]
Several approaches for image analysis of LSFM datasets have been introduced in recent years, but these methods are typically limited to selected organs and do not provide integrated whole-body quantification of elongated structures.
“Although several approaches for image analysis of light-sheet fluorescence microscopy (LSFM) datasets have been introduced in recent years6–10, these methods are typically limited to selected organs and do not provide integrated whole-body quantification of elongated structures.”
From A deep-learning framework reveals whole-body perturbations at cell level (2026) · cites this paper as [7]
Previous methods such as DeepMACT, DELiVR, AIMOS and SCP-Nano targeted a limited set of biological systems.
“Previous methods such as DeepMACT6 (tumour metastasis quantification in manually annotated organs), DELiVR7 (neuronal activation and microglial somata quantification and mapping in whole brain), AIMOS18 (segmentation of six organs for CT/LSFM) and SCP-Nano10 (nanocarrier targeted cell quantification in manually annotated organs) targeted a limited set of biological systems.”
From A deep-learning framework reveals whole-body perturbations at cell level (2026) · cites this paper as [7]
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References 63 · Cited by 38
Research works printed but not linked: 4
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- Authors
- Kaltenecker, D., Al-Maskari, R., Negwer, M., Hoeher, L., Kofler, F., Zhao, S., Todorov, M., 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.
- Affiliations
- University Hospital Heidelberg (DE)
- Helmholtz Munich (DE)
- TUM Klinikum (DE)
- Imperial College London (GB)
- University of Zurich (CH)
- Koç University (TR)
- Corresponding author
- Ertürk, Ali
- ORCID
- Venue
- Nature Methods
- Published
- 2024-04-22
- Volume
- 21
- Issue
- 7
- Pages
- 1306-1315
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- PMC
- PMC11239522
- OpenAlex
- W4395002428
- Cited by
- 38
- References
- 63
- Citations by year
- 2026: 8 · 2025: 19 · 2024: 11
- FWCI
- 7.33
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Nature Methods (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Zurich Open Repository and Archive (University of Zurich) (acceptedVersion, cc-by-nc-nd)
- Open access LMU (Ludwid Maxmilian's Universitat Munchen) (submittedVersion, other-oa)
- mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) (submittedVersion)
- PuSH - Publication Server of Helmholtz Zentrum München (submittedVersion)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion, cc-by)
- Zurich Open Repository and Archive (University of Zurich)
- Funders
- European Commission (HORIZON2020, 949017)
- Deutsche Forschungsgemeinschaft (SFB 1052, EXC 2145, EXC 2145SyNergy, 390857198, SFB824, TR 296, TR 296 project 03)
- Bundesministerium für Bildung und Forschung (01KX2121)
- Else Kröner-Fresenius-Stiftung (2020 EKSE.23)
- NOMIS Stiftung
- Karl-Franzens-Universität Graz
- Stiftung zur Erforschung der Vaskulären Demenz
- Helmholtz Zentrum München
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- cell detection, c-fos, neuronal activity, light-sheet imaging, whole-brain imaging, brain clearing, mouse brain, microglia, weight loss, Fiji plugin, Docker containers
- MeSH terms
- Virtual Reality, Deep Learning, Animals, Brain, Humans, Image Processing, Computer-Assisted, Neurons, Software, Proto-Oncogene Proteins c-fos, Mice
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