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Machine learning analysis of whole mouse brain vasculature

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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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Articles
https://doi.org/10.1038/s41592-020-0792-1
1Institute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Zentrum München, Neuherberg, Germany. 2Institute for Stroke and
Dementia Research (ISD), Ludwig-Maximilians-Universität (LMU), Munich, Germany. 3Graduate School of Neuroscience (GSN), Munich, Germany.
4Department of Computer Science, Technical University of Munich (TUM), Munich, Germany. 5Center for Translational Cancer Research of the TUM
(TranslaTUM), Munich, Germany. 6Munich School of Bioengineering, Technical University of Munich (TUM), Munich, Germany. 7Institute of Pharmacology
and Toxicology, University of Zurich (UZH), Zurich, Switzerland. 8Munich Cluster for Systems Neurology (SyNergy), Munich, Germany. 9German Center
for Neurodegenerative Diseases (DZNE), Munich, Germany. 10These authors contributed equally: Mihail Ivilinov Todorov, Johannes Christian Paetzold.
11These authors jointly supervised this work: Bjoern Menze, Ali Ertürk. e-mail: bjoern.menze@tum.de; erturk@helmholtz-muenchen.de
Changes in cerebrovascular structures are key indicators for a
large number of diseases affecting the brain. Primary angiop-
athies, vascular risk factors (for example, diabetes), traumatic
brain injury, vascular occlusion and stroke all affect the function of
the brain’s vascular network13. The hallmarks of Alzheimers dis-
ease, including tauopathy and amyloidopathy, can also lead to aber-
rant remodeling of blood vessels1,4, allowing capillary rarefaction to
be used as a marker for vascular damages5. Therefore, quantitative
analysis of the entire brain vasculature is pivotal to developing a
better understanding of brain function in physiological and patho-
logical states. However, quantifying micrometer-scale changes in
the cerebrovascular network of the brain has been difficult for two
main reasons.
First, labeling and imaging of the complete mouse brain vascu-
lature down to the smallest blood vessels has not yet been achieved.
Magnetic resonance imaging (MRI), micro-computed tomography
(micro-CT) and optical coherence tomography do not have suffi-
cient resolution to capture capillaries in bulk tissue68. Fluorescent
microscopy provides higher resolution, but can typically only
be applied to tissue sections up to 200 μm in thickness9. Recent
advances in tissue clearing could overcome this problem10, but so
far there has been no systematic description of all vessels of all sizes
in an entire brain in three dimensions (3D).
The second challenge relates to the automated analysis of large
3D imaging datasets with substantial variance in signal intensity
and signal-to-noise ratio (SNR) at different depths. Simple inten-
sity- and shape-based filtering approaches such as Frangi’s vessel-
ness filters and more advanced image processing methods with
local spatial adaptation cannot reliably differentiate vessels from
background in whole-brain scans11,12. Finally, imaging of the com-
plete vascular network of the brain at capillary resolution results
in datasets of terabyte size. Established image processing methods
do not scale well to terabyte-sized image volumes, as they do
not generalize well to large images, and require intensive manual
fine-tuning1315.
Here we present VesSAP (Vessel Segmentation & Analysis
Pipeline), a deep learning-based method for automated analysis of
the entire mouse brain vasculature, overcoming the above limita-
tions. VesSAP encompasses three major steps: (1) staining, clearing
and imaging of the mouse brain vasculature down to the capil-
lary level with two different dyes: wheat germ agglutinin (WGA)
and Evans blue (EB); (2) automatic segmentation and tracing of
the whole-brain vasculature data via CNNs; and (3) extraction of
vascular features for hundreds of brain regions after registration of
the data to the Allen brain atlas (Fig. 1). Our deep learning-based
approach for network extraction in cleared tissue is robust, despite
variations in signal intensities and structures, outperforms previ-
ous filter-based methods and reaches the quality of segmentation
achieved by human annotators. We applied VesSAP to the three
commonly used mouse strains C57BL/6J, CD1 and BALB/c.
Results
Vascular staining, DISCO clearing and imaging. To reliably stain
the entire vasculature, we used WGA and EB dyes, which can be
visualized in different fluorescence channels. We injected EB dye
into live mice 12 h before WGA perfusion, allowing its long-term
circulation to mark vessels under physiological conditions16, while
we perfused mice with WGA during fixation. We then performed
Machine learning analysis of whole mouse brain
vasculature
Mihail Ivilinov Todorov1,2,3,10, Johannes Christian Paetzold4,5,6,10, Oliver Schoppe4,5, Giles Tetteh4,
Suprosanna Shit4,5,6, Velizar Efremov4,7, Katalin Todorov-Völgyi2, Marco Düring2,8, Martin Dichgans2,8,9,
Marie Piraud4, Bjoern Menze 4,5,6,11 ✉ and Ali Ertürk 1,2,8,11 ✉
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 con-
volutional 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.
NATURE METHODS | VOL 17 | APRIL 2020 | 442–449 | www.nature.com/naturemethods
442
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... This shows that the segmentation approach is indeed transferable with respect to extracting the brain vasculature, but that further processing/improvement is required to remove nonspecific signals, such as those from the skin. v. Lastly, due to the increasing importance and prevalence of deep learning (DL) methods in biological sciences, particularly in cerebrovascular segmentation of preclinical models (Daetwyler et al., Biella, 2021;Todorov et al., 2020), there is a need to understand DL methods and their applicability to biomedical imaging data. We highlight several DL methods and show how these can be assessed against each other to understand their applicability and performance; specifically, the original U-Net (Ronneberger, Fischer, & Brox, 2015), SegNet (Badrinarayanan, Kendall, & Cipolla, 2017), and three modified versions of the original U-Net architecture (dU-Net). ...
... In this case, the vessel crosssectional intensities are single peaks with radial Gaussian-like distributions. This contrasts with biomedical image analysis, such as zebrafish transgenic reporter lines or mouse antibody stainings, where the cells that make up the vessel walls-called endothelial cells-are visualized (Kugler et al., 2018;Todorov et al., 2020). This leads to a cross-sectional double-peak (or ring-shaped signal) intensity distribution in lumenized vessels, whereas unlumenized (often very small) vessels display single peaks (Kugler et al., 2018;Todorov et al., 2020). ...
... This contrasts with biomedical image analysis, such as zebrafish transgenic reporter lines or mouse antibody stainings, where the cells that make up the vessel walls-called endothelial cells-are visualized (Kugler et al., 2018;Todorov et al., 2020). This leads to a cross-sectional double-peak (or ring-shaped signal) intensity distribution in lumenized vessels, whereas unlumenized (often very small) vessels display single peaks (Kugler et al., 2018;Todorov et al., 2020). Although this could be addressed experimentally by performing microangiography, this is (a) laborious and (b) only shows perfused vessels. ...
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... Since the concept of tissue oxygenation is tightly linked to the vasculature, we also inspected changes in the vasculature of the spinal cord by means of vascular staining and light sheet fluorescence microscopy (LSFM). By applying a newly developed deep learning-based framework, VesSAP (Vessel Segmentation & Analysis Pipeline) [40], we reveal a reduction in the perfused vascular network in the spinal cord of EAE. ...
... We used the vessel segmentation and analysis pipeline (VesSAP) [40] to quantify the vasculature changes in the lumbar spinal cord by firstly manually defining a mask for the CNS tissue only separating it from the vertebra and surrounding vasculature. Next, we ran the segmentation, preprocessing and feature extraction to obtain the total vessel length (sum of vessel centerline voxels), bifurcation density (sum of segmentation skeleton bifurcations), and average radius of vessels (distance of all centerline voxels to the nearest segmentation mask). ...
... Next, we ran the segmentation, preprocessing and feature extraction to obtain the total vessel length (sum of vessel centerline voxels), bifurcation density (sum of segmentation skeleton bifurcations), and average radius of vessels (distance of all centerline voxels to the nearest segmentation mask). All measures were then corrected by a constant (described as the in vivo space in [40]) to account for shrinkage due to fixation and clearing. ...
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... ultrasound, due to the lack of resolution necessary to visualize the vast network of microvessels extending beyond the major arterial branches of the circle of Willis. [9][10][11] Additionally, in vivo twophoton imaging and in vivo optical coherence tomography have limited imaging depth. 12,13 Although immunohistochemical and electron microscopy studies have also revealed crucial insights into the NVU major players, these modalities are often limited to localized brain regions, as they are not easily applied to whole-brain studies. ...
... 1(e)-1(g)]. 11,32,33 Although, LSFM is referred to as one major imaging category, there are several different types of light sheet microscope configurations. For example, types of LSFM can include selective plane illumination microscopy (SPIM) as well as inverted, multiview, Bessel beam, and stimulation emission depletion variations of SPIM. ...
... 34 Most studies of the vasculature using LSFM have primarily utilized the standard Gaussian form of SPIM. 11,32,34 However, the strengths of Bessel beam SPIM were recently demonstrated for capturing the high-fidelity imaging of the brain vasculature while avoiding the streaking artifacts that result from Gaussian illumination. 35 Importantly, LSFM resolution can vary widely depending on the microscope and objective lens used, but overall this method can achieve submicron cellular resolution imaging for whole threedimensional (3D) volumes. ...
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Significance: The cerebrovasculature has become increasingly recognized as a major player in overall brain health and many brain disorders. Although there have been several landmark studies to understand details of these crucially important structures in an anatomically defined area, brain-wide examination of the whole cerebrovasculature, including microvessels, has been challenging. However, emerging techniques, including tissue processing and three-dimensional (3D) microscopy imaging, enable neuroscientists to examine the total vasculature in the entire mouse brain. Aim: Here, we aim to highlight advances in these high-resolution 3D mapping methods including block-face imaging and light sheet fluorescent microscopy. Approach: We summarize latest mapping tools to understand detailed anatomical arrangement of the cerebrovascular network and the organizing principles of the neurovascular unit (NVU) as a whole. Results: We discuss biological insights gained from studies using these imaging methods and how these tools can be used to advance our understanding of the cerebrovascular network and related cell types in the entire brain. Conclusions: This review article will help to understand recent advance in high-resolution NVU mapping in mice and provide perspective on future studies.
... For example, Tahir et al. [19], Haft-Javaherian et al. [20] and Damseh et al. [21] used CNNs for segmentation of brain vasculature in two-or multi-photon images. Kirst et al. [7] and Todorov et al. [22] used CNN to segment mouse brain vessels imaged by light-sheet microscopy. Tetteh et al. [23] introduced DeepVesselNet, which uses 2.5D CNN instead of 3D CNN to segment vessels in 3D angiographic volumes. ...
... However, it does not work well for cavities in thick vessels. We also compare the proposed method with other similar methods, namely OTSU [34], VesSAP [22], 3D-Unet [33] and Vnet [35]. OTSU is a traditional segmentation method, and the other three are deep learning-based methods. ...
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... Many recent approaches have been developed for segmenting and analyzing large blood vessel datasets. [14][15][16][17][18][19] Such methodologies show excellent results even for large 3D volumes containing whole brain data. Still, there are two main advantages of Pyvane compared with the other methodologies. ...
... The first is that the default processors of Pyvane have been shown to provide good results for hundreds of images obtained from many different animals in previously published works. 5,13,[20][21][22] This contrasts with the recently developed approaches, which tested the algorithms on only three, 16 five, 14 nine, 19 and fifteen 15 microscopy images. Thus, it is expected that Pyvane can be easily adapted to new datasets. ...
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... Figure 7-figure supplement 3 provides an example of this potential using manual segmentation on a small patch of the data presented in Figure 7. Given that the manual segmentation of these vessels is relatively simple, albeit arduous, machine learning approaches (Hilbert et al., 2020;Tetteh et al., 2020) also seem promising, as they commonly perform well in visual tasks (LeCun et al., 2015;Rueckert et al., 2016;Zaharchuk et al., 2018) and have successfully been applied to large-scale vasculature segmentations of mouse tissue-cleared data (Todorov et al., 2020). In general, the main challenges for these algorithms include the small vessel size compared to the voxel size, the broad range of vessel diameters, and the high noise levels. ...
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... The capillary network has recently been described thoroughly by multiple teams [Tsai et al. 2009, Xiong et al. 2017, Kirst et al. 2020, Todorov et al. 2020, Ji et al. 2021. Until recently, a challenge upon the description of the capillary network was the size of the capillaries and their density in the parenchyma: technical advances overcame them and allowed us to better describe the capillary bed. ...
Thesis
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