Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

A three-dimensional virtual mouse generates synthetic training data for behavioral analysis

Abstract

We developed a three-dimensional (3D) synthetic animated mouse based on computed tomography scans that is actuated using animation and semirandom, joint-constrained movements to generate synthetic behavioral data with ground-truth label locations. Image-domain translation produced realistic synthetic videos used to train two-dimensional (2D) and 3D pose estimation models with accuracy similar to typical manual training datasets. The outputs from the 3D model-based pose estimation yielded better definition of behavioral clusters than 2D videos and may facilitate automated ethological classification.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: A realistic 3D mouse model suitable for generating labels for machine learning.
Fig. 2: Image-domain translation makes realistic videos of animated mouse models.

Data availability

All data and raw behavioral video images are available online at Open Science Framework: https://osf.io/h3ec5/ (ref. 2). Source data for Fig. 2 and Supplementary Figs. 13 are provided with this paper.

Code availability

All code and models are available on public repositories: https://github.com/ubcbraincircuits/mCBF (ref. 13) and https://osf.io/h3ec5/ (ref. 2); see workflow document for an overview of software steps. Code (KID score, U-GAT-IT, pose lifting and clustering) is set up for reproducible runs via a capsule on Code Ocean23.

References

  1. 1.

    Mathis, A. et al. DeepLabCut: markerless pose estimation of user-defined body parts with deep learning. Nat. Neurosci. 21, 1281–1289 (2018).

    CAS  Article  Google Scholar 

  2. 2.

    Bolanos, L. et al. Synthetic animated mouse (SAM), University of British Columbia, data and 3D-models. OSF https://doi.org/10.17605/OSF.IO/H3EC5 (2021).

  3. 3.

    Khmelinskii, A. et al. Articulated whole-body atlases for small animal image analysis: construction and applications. Mol. Imaging Biol. 13, 898–910 (2011).

    Article  Google Scholar 

  4. 4.

    Klyuzhin, I. S., Stortz, G. & Sossi, V. Development of a digital unrestrained mouse phantom with non-periodic deformable motion. In Proc. 2015 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) (IEEE, 2015); https://doi.org/10.1109/nssmic.2015.7582140

  5. 5.

    Li, S. et al. Deformation-aware unpaired image translation for pose estimation on laboratory animals. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) https://doi.org/10.1109/cvpr42600.2020.01317 (IEEE, 2020).

  6. 6.

    Radhakrishnan, S. & Kuo, C. J. Synthetic to real world image translation using generative adversarial networks. IEEE Conference Publication https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8493745 (IEEE, 2018).

  7. 7.

    The International Brain Laboratory et al. Standardized and reproducible measurement of decision-making in mice. Preprint at bioRXiv https://doi.org/10.1101/2020.01.17.909838 (2020).

  8. 8.

    Galiñanes, G. L., Bonardi, C. & Huber, D. Directional reaching for water as a cortex-dependent behavioral framework for mice. Cell Rep. 22, 2767–2783 (2018).

    Article  Google Scholar 

  9. 9.

    Kwak, I. S., Guo, J.-Z., Hantman, A., Kriegman, D. & Branson, K. Detecting the starting frame of actions in video. Preprint at https://arxiv.org/abs/1906.03340 (2019).

  10. 10.

    Kim, J., Kim, M., Kang, H. & Lee, K. U-GAT-IT: generative attentional networks with adaptive layer-instance normalization for image-to-image translation. Preprint at https://arxiv.org/abs/1907.10830 (2019).

  11. 11.

    Wang, Z., Simoncelli, E. P. & Bovik, A. C. Multiscale structural similarity for image quality assessment. In Proc. Thirty-Seventh Asilomar Conference on Signals, Systems Computers, 2003 Vol. 2 1398–1402 (2003).

  12. 12.

    Bińkowski, M., Sutherland, D. J., Arbel, M. & Gretton, A. Demystifying MMD GANs. Preprint at https://arxiv.org/abs/1801.01401 (2018).

  13. 13.

    Bolanos, L. et al. ubcbraincircuits/mCBF: mCBF. Zenodo https://doi.org/10.5281/zenodo.4563193 (2021).

  14. 14.

    Nath, T. et al. Using DeepLabCut for 3D markerless pose estimation across species and behaviors. Nat. Protoc. 14, 2152–2176 (2019).

    CAS  Article  Google Scholar 

  15. 15.

    Martinez, J., Hossain, R., Romero, J. & Little, J. J. A simple yet effective baseline for 3D human pose estimation. In Proc. 2017 IEEE International Conference on Computer Vision (ICCV) (IEEE, 2017); https://doi.org/10.1109/iccv.2017.288

  16. 16.

    Günel, S. et al. DeepFly3D, a deep learning-based approach for 3D limb and appendage tracking in tethered, adult Drosophila.eLife 8, e48571 (2019).

    Article  Google Scholar 

  17. 17.

    Hopkins, B. & Skellam, J. G. A new method for determining the type of distribution of plant individuals. Ann. Bot. 18, 213–227 (1954).

    Article  Google Scholar 

  18. 18.

    Levine, J. H. et al. Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis. Cell 162, 184–197 (2015).

    CAS  Article  Google Scholar 

  19. 19.

    Blondel, V. D., Guillaume, J.-L., Lambiotte, R. & Lefebvre, E. Fast unfolding of communities in large networks. J. Stat. Mech. 2008, P10008 (2008).

    Article  Google Scholar 

  20. 20.

    Vogt, N. Collaborative neuroscience. Nat. Methods 17, 22 (2020).

    CAS  Article  Google Scholar 

  21. 21.

    Wang, Z., Bovik, A. C., Sheikh, H. R. & Simoncelli, E. P. Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13, 600–612 (2004).

    Article  Google Scholar 

  22. 22.

    Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J. & Wojna, Z. Rethinking the inception architecture for computer vision. In Proc. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, 2016); https://doi.org/10.1109/cvpr.2016.308.

  23. 23.

    Bolaños, L. A. et al. A 3D virtual mouse generates synthetic training data for behavioral analysis. Code Ocean https://doi.org/10.24433/CO.5412865.v1 (2021).

Download references

Acknowledgements

This work was supported by a Canadian Institutes of Health Research (CIHR) FDN-143209, NIH R21, a Fondation Leducq grant, Brain Canada for the Canadian Neurophotonics Platform and a Canadian Partnership for Stroke Recovery Catalyst grant to T.H.M. H.R. is supported by grants from Natural Sciences and Engineering Research Council of Canada (NSERC). D.X. was supported in part by funding provided by Brain Canada, in partnership with Health Canada, for the Canadian Open Neuroscience Platform initiative. This work was supported by computational resources made available through the NeuroImaging and NeuroComputation Centre at the Djavad Mowafaghian Centre for Brain Health (RRID SCR_019086). N.L.F. is supported by an NSERC Discovery Grant. Micro-CT imaging was performed at the UBC Centre for High-Throughput Phenogenomics, a facility supported by the Canada Foundation for Innovation, British Columbia Knowledge Development Foundation and the UBC Faculty of Dentistry. We thank E. Koch and L. Raymond of the UBC Djavad Mowafaghian Centre for Brain Health for the mouse open field video.

Author information

Affiliations

Authors

Contributions

L.A.B., T.H.M., J.M.L. and D.X. developed the synthetic mouse concept. L.A.B. generated the mouse model and all main figure animations. C.D. made supplemental models. L.A.B., T.H.M., J.M.L. and D.X. wrote the manuscript. H.R. edited the manuscript and advised on algorithm selection and validation statistics. D.X. collected experimental data and performed data analysis. H.H., L.A.B., D.X. and P.K.G. performed modeling and data analysis. N.L.F. collected the computed tomography scans and advised on model selection and resolution.

Corresponding author

Correspondence to Timothy H. Murphy.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Methods thanks the anonymous reviewers for their contribution to the peer review of this work. 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.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Figs. 1–3 and Table 1.

Reporting Summary

Supplementary Video 1

Synthetic data tutorial.

Supplementary Video 2

Mouse model.

Supplementary Video 3

Style transfer U-GAT-IT model (wheel).

Supplementary Video 4

Style transfer U-GAT-IT model (open field).

Supplementary Video 5

Style transfer U-GAT-IT model (Janelia pellet reaching).

Supplementary Video 6

Model gallery.

Supplementary Video 7

Water reaching and grooming scenes.

Supplementary Video 8

DLC 3D pose lifting.

Supplementary Data 1

Source data Supplementary Fig. 1.

Supplementary Data 2

Source data Supplementary Fig. 2.

Supplementary Data 3

Source data Supplementary Fig. 3.

Source data

Source Data Fig. 2

Statistical source data.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Bolaños, L.A., Xiao, D., Ford, N.L. et al. A three-dimensional virtual mouse generates synthetic training data for behavioral analysis. Nat Methods 18, 378–381 (2021). https://doi.org/10.1038/s41592-021-01103-9

Download citation

Further reading

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing