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Geometric deep learning enables 3D kinematic profiling across species and environments

Nature Methods volume 18, pages 564–573 (2021)Cite this article

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Abstract

Comprehensive descriptions of animal behavior require precise three-dimensional (3D) measurements of whole-body movements. Although two-dimensional approaches can track visible landmarks in restrictive environments, performance drops in freely moving animals, due to occlusions and appearance changes. Therefore, we designed DANNCE to robustly track anatomical landmarks in 3D across species and behaviors. DANNCE uses projective geometry to construct inputs to a convolutional neural network that leverages learned 3D geometric reasoning. We trained and benchmarked DANNCE using a dataset of nearly seven million frames that relates color videos and rodent 3D poses. In rats and mice, DANNCE robustly tracked dozens of landmarks on the head, trunk, and limbs of freely moving animals in naturalistic settings. We extended DANNCE to datasets from rat pups, marmosets, and chickadees, and demonstrate quantitative profiling of behavioral lineage during development.

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Fig. 1: Fully 3D deep learning versus 2D-to-3D triangulation for naturalistic 3D pose detection.
Fig. 2: Rat 7M, a training and benchmark dataset for 3D pose detection.
Fig. 3: DANNCE outperforms DLC on rats with and without markers.
Fig. 4: Kinematic profiling of the mouse behavioral repertoire.
Fig. 5: DANNCE can report the ontogeny of behavioral complexity in rats.
Fig. 6: 3D tracking across the behavioral repertoire of a marmoset and chickadees.

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Data availability

The Rat 7M video and motion capture datasets are available at https://doi.org/10.6084/m9.figshare.c.5295370.v3. Mouse training labels, video, and DANNCE predictions are available at https://github.com/spoonsso/dannce/. Statistical source data for Figs. 1, 5 and 6 are included with the manuscript. Source data for Fig. 3 are available at https://doi.org/10.6084/m9.figshare.13884038. Marmoset data are subject to additional veterinary restrictions and can be made available upon request. Source data are provided with this paper.

Code availability

The code for DANNCE is available at https://github.com/spoonsso/dannce/ and https://doi.org/10.5281/zenodo.4567514 (ref. 61). Code for analyzing and plotting data is available at https://doi.org/10.5281/zenodo.4571521 (ref. 62). The code for labeling points in 3D is available at https://github.com/diegoaldarondo/Label3D/. The core functions used for the behavioral embedding23 are available at https://github.com/jessedmarshall/CAPTURE_demo.

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Acknowledgements

We thank K. Herrera for 3D renderings; M. Tadross for guidance on the text; G. Pho and K. Mizes for assistance with 2D behavioral tracking; M. Shah and the Harvard Center for Brain Science neuroengineers for technical assistance; T. Pereira for discussion; S. Gannon for marmoset labeling assistance; M. Applegate for assisting in chickadee arena design; E. Naumann for illustrations; and the Black Rock Forest Consortium for permission to catch chickadees. J.D.M. acknowledges support from the Helen Hay Whitney Foundation and NINDS (K99NS112597). K.S.S. from NIH (F32MH122995), D.E.A. from the NSF (DGE1745303), W.L.W. from Harvard College Research Program, D.G.C.H. from Kavli Neural Systems Institute and the Leon Levy Foundation, S.N.C. from the NIMH (F32MH123015), D.A. from Beckman Young Investigator and New York Stem Cell Foundation Robertson Investigator programs, F.W. from the NIH (U19NS107466), T.W.D. from the Donna Bernstein fund and the NIH (R01GM136972), and B.P.Ö. from SFARI (646706), the NIH (R01GM136972), and the Starr Family Foundation.

Author information

Author notes

  1. These authors contributed equally: Timothy W. Dunn, Jesse D. Marshall.

Authors and Affiliations

  1. Duke Forge and Duke AI Health, Duke University, Durham, NC, USA

    Timothy W. Dunn & David E. Carlson

  2. Duke Global Neurosurgery and Neurology Division, Department of Neurosurgery, Duke University, Durham, NC, USA

    Timothy W. Dunn

  3. Department of Organismic and Evolutionary Biology and Center for Brain Science, Harvard University, Cambridge, MA, USA

    Jesse D. Marshall, William L. Wang, Amanda J. Gellis & Bence P. Ölveczky

  4. Department of Neurobiology, Duke University, Durham, NC, USA

    Kyle S. Severson & Fan Wang

  5. Department of Brain and Cognitive Sciences, McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA

    Kyle S. Severson & Fan Wang

  6. Program in Neuroscience, Harvard University, Cambridge, MA, USA

    Diego E. Aldarondo

  7. Laboratory of Neural Systems, The Rockefeller University, New York, NY, USA

    David G. C. Hildebrand & Winrich A. Freiwald

  8. Department of Neuroscience, Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA

    Selmaan N. Chettih & Dmitriy Aronov

  9. Department of Civil and Environmental Engineering, Duke University, Durham, NC, USA

    David E. Carlson

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Contributions

The project was conceived by B.P.Ö., T.W.D., and J.D.M. T.W.D. conceived and developed DANNCE. J.D.M. and W.L.W. acquired and analyzed rat, low-resolution mouse, and rat-pup datasets. J.D.M. performed all behavioral analysis in pups and generated all behavioral maps. T.W.D. quantified DANNCE performance and performed mouse kinematic analysis. K.S.S. developed multicamera video-acquisition software, performed high-resolution experiments in mice, and contributed to the DANNCE codebase. D.E.A. developed labeling software, contributed to the DANNCE codebase, and assisted with rat-pup dataset analysis. D.G.C.H. performed marmoset experiments. S.N.C. performed chickadee experiments and contributed to the DANNCE codebase. A.J.G. assisted with rat-pup analysis. B.P.Ö., D.E.C., D.A., F.W. and W.A.F. provided financial support and project guidance. T.W.D. and J.D.M wrote the manuscript with input from all authors. D.E.A. and K.S.S. contributed equally to this work, and this will be noted as such in their CVs.

Corresponding authors

Correspondence to Timothy W. Dunn, Jesse D. Marshall or Bence P. Ölveczky.

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The authors declare no competing interests.

Additional information

Peer review information Nature Methods thanks Gonzalo de Polavieja, Benjamin Risse, and the other, anonymous, reviewer(s), 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. 1–13 and note.

Reporting Summary

Supplementary Video 1

Animated example of DANNCE tracking capabilities. Blender rendering, animated using real data, of DANNCE tracking capabilities. DANNCE can track the 3D whole-body landmarks of animals in a manner that is robust to occlusions and can be extended to diverse environments and species.

Supplementary Video 2

Finetuning 2D pose-detection networks with limited training data does not enable 3D tracking across multiple behaviors. Video recording of a rat behaving in an open field, with landmarks tracked by DeepLabCut, projected onto video frames (left). We trained DeepLabCut with 225 hand-labeled frames. Shown consecutively are the predictions for the markers on the animal’s head and trunk, and then across the full 20 marker set made using 6 cameras, and then the predictions made using 3 cameras. Right, concurrent wireframe representations of the landmarks in the animal’s egocentric reference frame. Video speed is 1.5 times real time.

Supplementary Video 3

DANNCE, but not DLC, tracks 3D pose in Rat 7M validation data. Video recording of a rat with markers behaving in an open field, with tracked 3D landmarks projected onto video frames (left). Right, concurrent wireframe representations of the tracked 3D landmarks. Shown consecutively are DLC and DANNCE predictions on the validation animal with 3 cameras and then with 6 cameras. Video speed is 3.33 times real time.

Supplementary Video 4

DANNCE can track 3D kinematics in rats using a single camera. Showed consecutively are reprojections of 3D predictions on new camera views from a validation animal not used for training, followed by 3D wireframe predictions for each of six cameras on validation frames in a training animal. All predictions come from passing single-camera volumes through the DANNCE network. The 3D wireframe plot axes are in units of millimeters. Video speed is four times real time.

Supplementary Video 5

DANNCE, but not DLC, tracks 3D markerless rat pose. Video recording of a markerless rat behaving in an open field, with DLC-tracked 3D landmarks projected onto 2 different cameras. Coloring of the predicted landmarks reveals prediction errors that then lead to poor 3D triangulation performance. DLC predictions are then shown concurrent with DANNCE predictions. In this final segment, projections of 3D predictions are plotted on video frames (left), together with wireframe representations of the 3D predictions (right). Predictions were made using six cameras for networks trained on Rat 7M and without any additional training data. Video speed is four times real time.

Supplementary Video 6

DANNCE readily extends to 3D tracking in mice. Video recording of a mouse behaving in an open field, with tracked landmarks projected onto video frames (left). Right, concurrent wireframe representations of the tracked 3D landmarks. Predictions were made using six cameras. Video speed is two times real time. The 3D wireframe plot axes are in units of millimeters.

Supplementary Video 7

DANNCE enables kinematic profiling across behaviors in mice. A series of examples of mouse behavior sampled from behavioral clusters distributed across different categories in the behavioral embedding space. For each cluster sampled (left), four different examples of the rat’s behavior in that cluster are shown, with DANNCE landmark predictions overlaid (right). Videos are shown at real time and repeated twice.

Supplementary Video 8

DANNCE recordings across behavioral development. A series of examples of video recordings with tracked landmarks reprojected (left) and DANNCE 3D predictions (right) across timepoints of behavioral development. Video speed is three times real time.

Supplementary Video 9

Markerless 3D tracking across the marmoset behavioral repertoire. Left, video recording of a marmoset behaving in a naturalistic enclosure, with tracked landmarks projected onto video frames. Right, corresponding DANNCE 3D landmark predictions. Video speed is four times real time. The 3D wireframe plot axes are in units of millimeters.

Supplementary Video 10

Markerless 3D tracking across the chickadee behavioral repertoire. Left, video recording of a chickadee behaving in a naturalistic enclosure, with tracked landmarks projected onto video frames. Right, concurrent wireframe representations of the tracked 3D landmarks. Video speed is two times real time.

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Statistical Source Data.

Source Data Fig. 5

Statistical Source Data.

Source Data Fig. 6

Zip file containing two .csv files of Statistical Source Data for marmosets and chickadees.

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Dunn, T.W., Marshall, J.D., Severson, K.S. et al. Geometric deep learning enables 3D kinematic profiling across species and environments. Nat Methods 18, 564–573 (2021). https://doi.org/10.1038/s41592-021-01106-6

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