{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Dunn TW"],"funding":["NIA NIH HHS","NIMH NIH HHS","NINDS NIH HHS","NIGMS NIH HHS"],"pagination":["564-573"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8530226"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["18(5)"],"pubmed_abstract":["Comprehensive descriptions of animal behavior require precise three-dimensional (3D) measurements of whole-body movements. Although two-dimensional approaches can track visible landmarks in restrictive environments, performance drops in freely moving animals, due to occlusions and appearance changes. Therefore, we designed DANNCE to robustly track anatomical landmarks in 3D across species and behaviors. DANNCE uses projective geometry to construct inputs to a convolutional neural network that leverages learned 3D geometric reasoning. We trained and benchmarked DANNCE using a dataset of nearly seven million frames that relates color videos and rodent 3D poses. In rats and mice, DANNCE robustly tracked dozens of landmarks on the head, trunk, and limbs of freely moving animals in naturalistic"],"journal":["Nature methods"],"pubmed_title":["Geometric deep learning enables 3D kinematic profiling across species and environments."],"pmcid":["PMC8530226"],"funding_grant_id":["F32 MH123015","U19 NS107466","F32 MH122995","R01 NS099323","K99 NS112597","DP2 AG071918","R01 GM136972"],"pubmed_authors":["Hildebrand DGC","Marshall JD","Chettih SN","Aronov D","Wang F","Wang WL","Severson KS","Olveczky BP","Aldarondo DE","Gellis AJ","Freiwald WA","Dunn TW","Carlson DE"],"additional_accession":[]},"is_claimable":false,"name":"Geometric deep learning enables 3D kinematic profiling across species and environments.","description":"Comprehensive descriptions of animal behavior require precise three-dimensional (3D) measurements of whole-body movements. Although two-dimensional approaches can track visible landmarks in restrictive environments, performance drops in freely moving animals, due to occlusions and appearance changes. Therefore, we designed DANNCE to robustly track anatomical landmarks in 3D across species and behaviors. DANNCE uses projective geometry to construct inputs to a convolutional neural network that leverages learned 3D geometric reasoning. We trained and benchmarked DANNCE using a dataset of nearly seven million frames that relates color videos and rodent 3D poses. In rats and mice, DANNCE robustly tracked dozens of landmarks on the head, trunk, and limbs of freely moving animals in naturalistic","dates":{"release":"2021-01-01T00:00:00Z","publication":"2021 May","modification":"2026-05-09T09:57:51.523Z","creation":"2022-02-11T12:22:45.958Z"},"accession":"S-EPMC8530226","cross_references":{"pubmed":["33875887"],"doi":["10.1038/s41592-021-01106-6"]}}