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

Clicks: 322
ID: 100053
2019
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Abstract
Studying how neural circuits orchestrate limbed behaviors requires the precise measurement of the positions of each appendage in three-dimensional (3D) space. Deep neural networks can estimate two-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, . Here, we present DeepFly3D, a software that infers the 3D pose of tethered, adult 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 behavioral measurements at an unprecedented level of detail for a variety of biological applications.
Reference Key
gunel2019deepfly3delife Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Günel, Semih;Rhodin, Helge;Morales, Daniel;Campagnolo, João;Ramdya, Pavan;Fua, Pascal;
Journal eLife
Year 2019
DOI
10.7554/eLife.48571
URL
Keywords
Deep learning neuroscience computer vision d. melanogaster 3d pose estimation animal behavior unsupervised classification

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