ArticleThe Journal of experimental biology2022
Validating markerless pose estimation with 3D X-ray radiography.
Article in The Journal of experimental biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Lightning Pose 3D: an uncertainty-aware framework for data-efficient multi-view animal pose estimation.bioRxiv : the preprint server for biology · 2026Article
- The dynamic 3D horse: analyzing the relationship between whole body pathomechanics and joint degeneration in the fetlocks.Frontiers in veterinary science · 2026Article
- Accurate Tracking of Locomotory Kinematics in Mice Moving Freely in Three-Dimensional Environments.eNeuro · 2025Article
- AI-assisted 3D analysis of grasping and reaching behavior of squirrel monkeys during recovery from cervical spinal cord injury.Behavioural brain research · 2025Article
- The impact of machine learning on ethological neuroscience.Frontiers in behavioral neuroscience · 2025Review
- A dynamic subset of network interactions underlies tuning to natural movements in marmoset sensorimotor cortex.Nature communications · 2024Article
- A dynamic subset of network interactions underlies tuning to natural movements in marmoset sensorimotor cortex.Research square · 2023Article
- Modern three-dimensional digital methods for studying locomotor biomechanics in tetrapods.The Journal of experimental biology · 2023Review
- Estimation of skeletal kinematics in freely moving rodents.Nature methods · 2022Article
- Validating markerless pose estimation with 3D X-ray radiography.The Journal of experimental biology · 2022Article
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Authors and funding
4 authors.
Funding
Abstract
To reveal the neurophysiological underpinnings of natural movement, neural recordings must be paired with accurate tracking of limbs and postures. Here, we evaluated the accuracy of DeepLabCut (DLC), a deep learning markerless motion capture approach, by comparing it with a 3D X-ray video radiography system that tracks markers placed under the skin (XROMM). We recorded behavioral data simultaneously with XROMM and RGB video as marmosets foraged and reconstructed 3D kinematics in a common coordinate system. We used the toolkit Anipose to filter and triangulate DLC trajectories of 11 markers on the forelimb and torso and found a low median error (0.228 cm) between the two modalities corresponding to 2.0% of the range of motion. For studies allowing this relatively small error, DLC and similar markerless pose estimation tools enable the study of increasingly naturalistic behaviors in many fields including non-human primate motor control.
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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.