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Automated analysis of whole brainvasculature using machine learning.
bioRxiv, accepted (2019)
Tissue clearing methods enable imaging of intactbiological specimens without sectioning. Howev-er, reliable and scalable analysis of such largeimaging data in 3D remains a challenge. Towardsthis goal, we developed a deep learning-basedframework to quantify and analyze the brain vas-culature, named Vessel Segmentation & AnalysisPipeline ( VesSAP). Our pipeline uses a fully con-volutional network with a transfer learning a p-proach for segmentation. We systematically ana-lyzed vascular features of the whole brains i n-cluding their length, bifurcation points and radiusat the micrometer scale by registering them to the Allen mouse brain atlas. We reported the firstevidence of secondary intracranial collateral vas-cularization in CD1-Elite mice and found reducedvascularization in the brainstem as compared to the cerebrum. VesSAP thus enables unbiasedand scalable quantifications for the angioarchi-tecture of the cleared intact mouse brain andyields new biological insights related to the vas-cular brain function.
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Publication type
Article: Journal article
Document type
Scientific Article
Language
english
Publication Year
2019
HGF-reported in Year
2019
Journal
bioRxiv
Publisher
Cold Spring Harbor Laboratory Press
Publishing Place
Cold Spring Harbor
Reviewing status
Peer reviewed
Institute(s)
Institute for Tissue Engineering and Regenerative Medicine (ITERM)
Erfassungsdatum
2019-10-23