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DeepFly3D, a deep learning-based approach for 3D limb and appendage tracking in tethered, adult Drosophila

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Cite this article as: eLife 2019;8:e48571 doi: 10.7554/eLife.48571

Abstract

Studying how neural circuits orchestrate limbed behaviors requires the precise measurement of the positions of each appendage in 3-dimensional (3D) space. Deep neural networks can estimate 2-dimensional (2D) pose in freely behaving and tethered animals. However, the unique challenges associated with transforming these 2D measurements into reliable and precise 3D poses have not been addressed for small animals including the fly, Drosophila melanogaster. Here we present DeepFly3D, a software that infers the 3D pose of tethered, adult Drosophila using multiple camera images. DeepFly3D does not require manual calibration, uses pictorial structures to automatically detect and correct pose estimation errors, and uses active learning to iteratively improve performance. We demonstrate more accurate unsupervised behavioral embedding using 3D joint angles rather than commonly used 2D pose data. Thus, DeepFly3D enables the automated acquisition of Drosophila behavioral measurements at an unprecedented level of detail for a variety of biological applications.

Article and author information

Author details

  1. Semih Günel

    School of Computer and Communication Sciences, Computer Vision Laboratory, EPFL, Lausanne, Switzerland
    For correspondence
    semih.gunel@epfl.ch
    Competing interests
    The authors declare that no competing interests exist.
  2. Helge Rhodin

    School of Computer and Communication Sciences, Computer Vision Laboratory, EPFL, Lausanne, Switzerland
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2692-0801
  3. Daniel Morales

    School of Life Sciences, Brain Mind Institute and Interfaculty Institute of Bioengineering, Neuroengineering Laboratory, EPFL, Lausanne, Switzerland
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7469-0898
  4. João H Campagnolo

    School of Life Sciences, Brain Mind Institute and Interfaculty Institute of Bioengineering, Neuroengineering Laboratory, EPFL, Lausanne, Switzerland
    Competing interests
    The authors declare that no competing interests exist.
  5. Pavan Ramdya

    School of Life Sciences, Brain Mind Institute and Interfaculty Institute of Bioengineering, Neuroengineering Laboratory, EPFL, Lausanne, Switzerland
    For correspondence
    pavan.ramdya@epfl.ch
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5425-4610
  6. Pascal Fua

    School of Computer and Communication Sciences, Computer Vision Laboratory, EPFL, Lausanne, Switzerland
    Competing interests
    The authors declare that no competing interests exist.

Funding

Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (175667)

  • Daniel Morales
  • Pavan Ramdya

Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (181239)

  • Daniel Morales
  • Pavan Ramdya

EPFL (iPhD)

  • Semih Günel

Microsoft Research (JRC Project)

  • Helge Rhodin

Swiss Government Excellence Postdoctoral Scholarship (2018.0483)

  • Daniel Morales

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Timothy O'Leary, University of Cambridge, United Kingdom

Publication history

  1. Received: May 18, 2019
  2. Accepted: September 28, 2019
  3. Accepted Manuscript published: October 4, 2019 (version 1)

Copyright

© 2019, Günel et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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