ArticleNature neuroscience2018
DeepLabCut: markerless pose estimation of user-defined body parts with deep learning.
Article in Nature neuroscience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04074772 (Leveraging Machine Learning to Effortlessly Track Patient Movement in the Clinic.), which is not on this map. Cited by 2,500 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Leveraging Machine Learning to Effortlessly Track Patient Movement in the Clinic.
Who cites it
2,500 citing papers in PubMed.
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- Realignment of representational drift in mouse visual cortex via flexible electrode arrays.Nature biomedical engineering · 2026Article
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- Deep Learning-Based Quantification of Spontaneous Pain Behaviours in Mice.European journal of pain (London, England) · 2026Article
- Early life experiences, the neural epigenome and affective disorder risk.Nature reviews. Neuroscience · 2026Review
2,440 more citing papers are in PubMed but not listed here.
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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30127430What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.