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Machine learning analysis of whole mouse brain vasculature
Open accessAbstract
Tissue clearing methods enable the imaging of biological specimens without sectioning. However, reliable and scalable analysis of large imaging datasets in three dimensions remains a challenge. Here we developed a deep learning-based framework to quantify and analyze brain vasculature, named Vessel Segmentation & Analysis Pipeline (VesSAP). Our pipeline uses a convolutional neural network (CNN) with a transfer learning approach for segmentation and achieves human-level accuracy. By using VesSAP, we analyzed the vascular features of whole C57BL/6J, CD1 and BALB/c mouse brains at the micrometer scale after registering them to the Allen mouse brain atlas. We report evidence of secondary intracranial collateral vascularization in CD1 mice and find reduced vascularization of the brainstem in comparison to the cerebrum. Thus, VesSAP enables unbiased and scalable quantifications of the angioarchitecture of cleared mouse brains and yields biological insights into the vascular function of the brain.
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“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 [9]
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“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 [9]
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Metadata
- Authors
- Todorov, M. I., Paetzold, J. C., Schoppe, O., Tetteh, G., Shit, S., Efremov, V., Todorov-Völgyi, K., Düring, M., Dichgans, M., Piraud, M., Menze, B., & Ertürk, A.
- Affiliations
- Helmholtz Munich (DE)
- Technical University of Munich (DE)
- University of Zurich (CH)
- German Center for Neurodegenerative Diseases (DE)
- Corresponding author
- Menze, Bjoern
- Ertürk, Ali
- ORCID
- Venue
- Nature Methods
- Published
- 2020-03-11
- Volume
- 17
- Issue
- 4
- Pages
- 442-449
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- PMC
- PMC7591801
- OpenAlex
- W3012387312
- Cited by
- 338
- References
- 51
- Citations by year
- 2026: 32 · 2025: 45 · 2024: 69 · 2023: 54 · 2022: 56 · 2021: 61 · 2020: 20
- FWCI
- 18.56
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Nature Methods (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Open access LMU (Ludwid Maxmilian's Universitat Munchen) (submittedVersion)
- Site cant be reached (submittedVersion)
- DZNE Pub (submittedVersion)
- PuSH - Publication Server of Helmholtz Zentrum München (submittedVersion)
- Funders
- National Institute on Aging (RF1AG057575)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Retinal Imaging and Analysis
- Keywords
- cerebral vasculature, vessel segmentation, tissue clearing, whole-brain imaging, convolutional neural networks, transfer learning, Allen Mouse Brain Atlas, intracranial collateral circulation, mouse brain, angioarchitecture
- MeSH terms
- Machine Learning, Animals, Brain, Imaging, Three-Dimensional, Mice
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