3D mouse pose from single-view video and a new dataset.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
21 08 2023
21 08 2023
Historique:
received:
25
11
2022
accepted:
16
08
2023
medline:
23
8
2023
pubmed:
22
8
2023
entrez:
21
8
2023
Statut:
epublish
Résumé
We present a method to infer the 3D pose of mice, including the limbs and feet, from monocular videos. Many human clinical conditions and their corresponding animal models result in abnormal motion, and accurately measuring 3D motion at scale offers insights into health. The 3D poses improve classification of health-related attributes over 2D representations. The inferred poses are accurate enough to estimate stride length even when the feet are mostly occluded. This method could be applied as part of a continuous monitoring system to non-invasively measure animal health, as demonstrated by its use in successfully classifying animals based on age and genotype. We introduce the Mouse Pose Analysis Dataset, the first large scale video dataset of lab mice in their home cage with ground truth keypoint and behavior labels. The dataset also contains high resolution mouse CT scans, which we use to build the shape models for 3D pose reconstruction.
Identifiants
pubmed: 37604955
doi: 10.1038/s41598-023-40738-w
pii: 10.1038/s41598-023-40738-w
pmc: PMC10442417
doi:
Types de publication
Dataset
Langues
eng
Sous-ensembles de citation
IM
Pagination
13554Informations de copyright
© 2023. Springer Nature Limited.
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