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

The DOI matches its Crossref record; 52 of the 52 references it cites were found in a registry or an index.

Machine learning analysis of whole mouse brain vasculature

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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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Research works printed but not linked: 5

  1. Menti, E., Bonaldi, L., Ballerini, L., Ruggeri, A. & Trucco, E. In International Workshop on Simulation and Synthesis in Medical Imaging 167–176 (Springer, Year).
  2. Kingma, D. P. & Ba, J. Adam: a method for stochastic optimization. Preprint at https://arxiv.org/abs/1412.6980 (2017). · arXiv
  3. Abadi, M. et al. in 12th USENIX Symposium on Operating Systems Design and Implementation 265–283 (2016).
  4. Paetzold, J. C. et al. clDice—a novel connectivity-preserving loss function for vessel segmentation. in Medical Imaging Meets NeurIPS 2019 Workshop (2019).
  5. Cohen, J. The effect size index: d. Stat. Power Anal. Behav. Sci. 2, 284–288 (1988).

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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
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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