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

Machine learning analysis of whole mouse brain vasculature

Open access
Todorov, Mihail Ivilinov, Paetzold, Johannes Christian, Schoppe, Oliver, Tetteh, Giles, Shit, Suprosanna, Efremov, Velizar, Todorov-Völgyi, Katalin, Düring, Marco +4 more·2020·Nature Methods

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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  • 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.”
  • 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.”

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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
Venue
Nature Methods
Published
2020-03-11
Volume
17
Issue
4
Pages
442-449
ISSN
  • 1548-7091 (ISSN-L)
  • 1548-7105
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
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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