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Machine learning analysis of whole mouse brain vasculature

Nature Methods volume 17, pages 442–449 (2020)Cite this article

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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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Fig. 1: Summary of the VesSAP pipeline.
Fig. 2: Enhancement of vascular staining using two complementary dyes.
Fig. 3: Deep learning architecture of VesSAP and performance on vessel segmentation.
Fig. 4: Pipeline showing the feature extraction and registration process.
Fig. 5: Anatomical properties of the neurovasculature in adult mouse brain mapped to the Allen brain atlas clusters.
Fig. 6: Exemplary quantitative analysis enabled by VesSAP.

Data availability

VesSAP data are publicly hosted at http://DISCOtechnologies.org/VesSAP and include original scans and registered atlas data.

Code availability

VesSAP codes are publicly hosted at http://DISCOtechnologies.org/VesSAP and include the imaging protocol, trained algorithms, training data and a reference set of features describing the vascular network in all brain regions. Additionally, the source code is hosted on GitHub (https://github.com/vessap/vessap) and on the executable platform Code Ocean (https://doi.org/10.24433/CO.1402016.v1)52. Implementation of external libraries is available on request.

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Acknowledgements

This work was supported by the Vascular Dementia Research Foundation, Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy within the framework of the Munich Cluster for Systems Neurology (EXC 2145 SyNergy, ID 390857198), ERA-Net Neuron (01EW1501A to A.E.), Fritz Thyssen Stiftung (to A.E.; reference no. 10.17.1.019MN), a DFG research grant (to A.E.; reference no. ER 810/2-1), the Helmholtz ICEMED Alliance (to A.E.), the NIH (to A.E.; reference no. AG057575) and the German Federal Ministry of Education and Research via the Software Campus initiative (to O.S.). S.S. is supported by the Translational Brain Imaging Training Network (TRABIT) under the European Union’s Horizon 2020 research and innovation program (grant agreement ID 765148). Furthermore, NVIDIA supported this work via the GPU Grant Program. V.E. was funded by Human Brain Project (HBP SGA 2, 785907). M.I.T. is a member of the Graduate School of Systemic Neurosciences (GSN), Ludwig Maximilian University of Munich. We thank R. Cai, C. Pan, F. Voigt, I. Ezhov, A. Sekuboyina, M. Goergens, F. Hellal, R. Malik, U. Schillinger and T. Wang for technical advice and C. Heisen for critical reading of the manuscript.

Author information

Author notes

  1. These authors contributed equally: Mihail Ivilinov Todorov, Johannes Christian Paetzold.

  2. These authors jointly supervised this work: Bjoern Menze, Ali Ertürk.

Authors and Affiliations

  1. Institute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Zentrum München, Neuherberg, Germany

    Mihail Ivilinov Todorov & Ali Ertürk

  2. Institute for Stroke and Dementia Research (ISD), Ludwig-Maximilians-Universität (LMU), Munich, Germany

    Mihail Ivilinov Todorov, Katalin Todorov-Völgyi, Marco Düring, Martin Dichgans & Ali Ertürk

  3. Graduate School of Neuroscience (GSN), Munich, Germany

    Mihail Ivilinov Todorov

  4. Department of Computer Science, Technical University of Munich (TUM), Munich, Germany

    Johannes Christian Paetzold, Oliver Schoppe, Giles Tetteh, Suprosanna Shit, Velizar Efremov, Marie Piraud & Bjoern Menze

  5. Center for Translational Cancer Research of the TUM (TranslaTUM), Munich, Germany

    Johannes Christian Paetzold, Oliver Schoppe, Suprosanna Shit & Bjoern Menze

  6. Munich School of Bioengineering, Technical University of Munich (TUM), Munich, Germany

    Johannes Christian Paetzold, Suprosanna Shit & Bjoern Menze

  7. Institute of Pharmacology and Toxicology, University of Zurich (UZH), Zurich, Switzerland

    Velizar Efremov

  8. Munich Cluster for Systems Neurology (SyNergy), Munich, Germany

    Marco Düring, Martin Dichgans & Ali Ertürk

  9. German Center for Neurodegenerative Diseases (DZNE), Munich, Germany

    Martin Dichgans

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  1. Mihail Ivilinov Todorov
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Contributions

M.I.T. performed the tissue processing, clearing and imaging experiments. M.I.T. and K.T.-V. developed the whole-brain staining protocol. M.I.T. stitched and assembled the whole-brain scans. V.E. and J.C.P. generated the synthetic vascular training dataset. J.C.P., G.T. and O.S. developed the deep learning architecture and trained the models. J.C.P. and S.S. performed the quantitative analyses. M.I.T. annotated the data. M. Düring and M. Dichgans helped with data interpretation. B.M., M.P. and G.T. provided guidance in developing the deep learning architecture and helped with data interpretation. A.E., M.I.T., B.M. and J.C.P. wrote the manuscript. All authors edited the manuscript. A.E. initiated and led all aspects of the project.

Corresponding authors

Correspondence to Bjoern Menze or Ali Ertürk.

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The authors declare no competing interests.

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Peer review information Nina Vogt was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Integrated supplementary information

Supplementary Figure 1 Vasculature of a CD1 mouse, stained with WGA and EB.

a, Sagittal maximum intensity projections. b, Coronal maximum intensity projections. c, Axial maximum projections. d-f, Close-ups where capillary level staining is evident. The experiment was performed 9 times with similar results.

Supplementary Figure 2 Experimental measurement of the point spread function (PSF) of the LaVision light-sheet Ultramicroscope II.

a, Red fluorescent beads (diameter 0.1 µm) were embedded in 1% agarose gel and cleared using 3DISCO. The beads were then imaged in BABB medium (RI = 1.56) using 4× objective lens (Olympus XLFLUOR 340), at 580/25 nm excitation and with a 625/30 nm emission filter by sampling at 1.625 × 1.625 × 1 µm. b, Full width half maximum (FWHM) measure derived from the Gaussian fit to the intensity profile, along the indicated cross-sections in the center of the diffraction pattern (a) of an exemplary bead. c, Quantification of the PSF distribution (n = 6) derived from the Gaussian fittings. All data values are given as mean ± SEM.

Supplementary Figure 3 Validation of complimentary staining of the neurovasculature.

a,b, Maximum intensity projection of confocal microscopy imaging of WGA and EB signal respectively. c, Merging of the two signals. d-h, Maximum intensity projections of the light-sheet microscopy imaging of a representative C57BL/6J specimen stained with EB, showing the major vascular segments in different planes. The experiment was performed 3 times with similar results.

Supplementary Figure 4 Raw signal intensity distribution along line profiles across stained vessels for three animals.

Fluorescence signal profiles for WGA and EB plotted based on vessel size. Data are separated based on WGA and EB signal intensity: a) comparable WGA and EB signal intensity, b) Signal intensity is stronger for WGA than for EB, c) Signal intensity is stronger for EB than for WGA.

Supplementary Figure 5 Comparison of the signal strength of anti-CD31 and lectin dyes.

a-b, Axial maximum intensity projection of 150 µm thick tissue, stained as indicated. c, SNR quantifications on the line profiles indicated in (a) and (b) with warm and cold colored lines for small and large sized segments, respectively. The red arrowheads indicate where the signal of the vasculature gets higher. The experiments were performed on one mouse per condition.

Supplementary Figure 6 Demonstration of the synthetic data used for VesSAP.

3D visualization including radius information in pixels (px) for one exemplary volume of synthetic data, which was used for pre-training our model in our transfer learning approach.

Supplementary Figure 7 Details of the segmentation quality by VesSAP.

a, b, Side by side slices of the raw WGA channel image (a) and the segmentation (b). c, 3D rendering of a small brain volume. The experiment was performed on 9 different mice with similar results.

Supplementary Figure 8 Three spaces of reported features.

Visualization of the three distinct spaces, in which we report the extracted features. The steps to account for the Euclidean length and the tissue shrinkage are visualized with an exemplary calculation of the vessel length of three vessel pixels in a 2D plane.

Supplementary Figure 9 Regression analysis of the neurovasculature in mouse strains.

Scatter plot of the local vessel length against the local bifurcation density (Pearson’s r = 0.9657; p = 1.7 × 10-125). Each point represents the mean of three animals per strain.

Supplementary information

Supplementary Information

Supplementary Figs. 1–9 and Supplementary Tables 1–12

Reporting Summary

Supplementary Video 1

Visualization of WGA and EB double staining using confocal microscopy. Representative tissue visualization of WGA and EB double staining of the cerebrovasculature showing that vessels are not collapsed but retain their circular shape (for example, the white arrow along the vessel). WGA is shown in white, and EB is shown in magenta. The experiment was performed three times with similar results.

Supplementary Video 2

Visualization of WGA and EB double staining using light-sheet microscopy. Visualization of a representative CD1 mouse brain by VesSAP. The experiment was performed three times with similar results.

Supplementary Video 3

Virtual reality-optimized visualization of the cerebrovasculature of the intact brain in Vision4D. The whole mouse brain shown in Supplementary Video 2 has been rendered for virtual reality viewing by using Arivis InViewR. Please see http://DISCOtechnologies.org/VesSAP/#VR for further information on how to view this virtual reality video.

Supplementary Video 4

Detailed visualization of vascular segmentation and feature extraction from light-sheet data. Segmentation and features demonstration on a subset of the whole dataset. VesSAP enables reliable segmentation (red) and feature extraction (bifurcation points and centerlines in green and cyan, respectively) down to the capillary level from imaging data (gray). The experiment was performed on nine mice with similar results.

Supplementary Video 5

Overall visualization of registration of the segmentation to the Allen brain atlas in Imaris. Whole-brain data registered to the Allen adult brain atlas. The experiment was performed on nine mice with similar results.

Supplementary Video 6

Detailed visualization of the registration and segmentation in Vision4D. Substack of the whole-brain data registered to the Allen adult brain atlas. This video shows full-resolution segmentation on a small set of the brain scan data. The experiment was performed nine times with similar results.

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Todorov, M.I., Paetzold, J.C., Schoppe, O. et al. Machine learning analysis of whole mouse brain vasculature. Nat Methods 17, 442–449 (2020). https://doi.org/10.1038/s41592-020-0792-1

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