Machine learning analysis of whole mouse brain vasculature.
Journal
Nature methods
ISSN: 1548-7105
Titre abrégé: Nat Methods
Pays: United States
ID NLM: 101215604
Informations de publication
Date de publication:
04 2020
04 2020
Historique:
received:
19
04
2019
accepted:
14
02
2020
pubmed:
13
3
2020
medline:
8
7
2020
entrez:
13
3
2020
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 32161395
doi: 10.1038/s41592-020-0792-1
pii: 10.1038/s41592-020-0792-1
pmc: PMC7591801
mid: NIHMS1628795
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
442-449Subventions
Organisme : NIA NIH HHS
ID : RF1 AG057575
Pays : United States
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