Cited by 343Medical Image Segmentation TechniquesCell Image Analysis TechniquesRetinal Imaging and Analysis

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Machine learning analysis of whole mouse brain vasculature

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Todorov, Mihail Ivilinov, Paetzold, Johannes Christian, Schoppe, Oliver, Tetteh, Giles, Shit, Suprosanna, Efremov, Velizar, Todorov-Völgyi, Katalin, Düring, Marco, Dichgans, Martin, Piraud, Marie, Menze, Bjoern, Ertürk, Ali·2020·Nature Methods

DOI 10.1038/s41592-020-0792-1PMC PMC7591801

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Abstract

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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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: Todorov, Mihail Ivilinov
Paetzold, Johannes Christian
Schoppe, Oliver
Shit, Suprosanna
Dichgans, Martin
Piraud, Marie
Menze, Bjoern
Ertürk, Ali
Venue: Nature Methods
Publisher: Springer Science and Business Media LLC
Published: 2020-03-11
Volume: 17
Issue: 4
Pages: 442-449
ISSN: 1548-7091 (ISSN-L)
1548-7105
DOI: 10.1038/s41592-020-0792-1
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
Licence: www.springer.com/tdm (tdm)
www.springer.com/tdm (vor)
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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Machine learning analysis of whole mouse brain vasculature

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