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

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, 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

SUBMITTER: Gunel S 

PROVIDER: S-EPMC6828327 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature