104
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: not found
      • Article: not found

      DeepLabCut: markerless pose estimation of user-defined body parts with deep learning

      Read this article at

      ScienceOpenPublisherPubMed
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Quantifying behavior is crucial for many applications in neuroscience. Videography provides easy methods for the observation and recording of animal behavior in diverse settings, yet extracting particular aspects of a behavior for further analysis can be highly time consuming. In motor control studies, humans or other animals are often marked with reflective markers to assist with computer-based tracking, but markers are intrusive, and the number and location of the markers must be determined a priori. Here we present an efficient method for markerless pose estimation based on transfer learning with deep neural networks that achieves excellent results with minimal training data. We demonstrate the versatility of this framework by tracking various body parts in multiple species across a broad collection of behaviors. Remarkably, even when only a small number of frames are labeled (~200), the algorithm achieves excellent tracking performance on test frames that is comparable to human accuracy.

          Related collections

          Author and article information

          Journal
          Nature Neuroscience
          Nat Neurosci
          Springer Nature America, Inc
          1097-6256
          1546-1726
          August 20 2018
          Article
          10.1038/s41593-018-0209-y
          30127430
          fd263dca-e51b-4d06-982c-23ba7db1d4f0
          © 2018

          http://www.springer.com/tdm

          History

          Comments

          Comment on this article