Journal
NATURE METHODS
Volume 17, Issue 4, Pages 442-+Publisher
NATURE PORTFOLIO
DOI: 10.1038/s41592-020-0792-1
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Funding
- Vascular Dementia Research Foundation
- Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy [EXC 2145 SyNergy, 390857198]
- ERA-Net Neuron [01EW1501A]
- Fritz Thyssen Stiftung [10.17.1.019MN]
- DFG research grant [ER 810/2-1]
- Helmholtz ICEMED Alliance
- NIH [AG057575]
- German Federal Ministry of Education and Research via the Software Campus initiative
- Translational Brain Imaging Training Network (TRABIT) under the European Union's Horizon 2020 research and innovation program [765148]
- NVIDIA via the GPU Grant Program
- Human Brain Project (HBP SGA 2) [785907]
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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. VesSAP is a tissue clearing- and deep learning-based pipeline for comprehensively analyzing mouse vasculature, from large vessels to small capillaries.
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