DOI: 10.1002/ame2.12530
High-throughput markerless pose estimation and home-cage activity analysis of tree shrew using deep learning
Yangzhen Wang1
Feng Su2
Rixu Cong3
Mengna Liu4
Kaichen Shan1
Xiaying Li5
Desheng Zhu5
Yusheng Wei5
Jiejie Dai6
Chen Zhang4
Yonglu Tian7
1.Department of Automation,Tsinghua University,Beijing,China2.College of Future Technology,Peking University,Beijing,China3.Ministry of Education,Key Laboratory of Cell Proliferation and Differentiation,College of Life Sciences,Peking University,Beijing,China4.School of Basic Medical Sciences,Beijing Key Laboratory of Neural Regeneration and Repair,Advanced Innovation Center for Human Brain Protection,Capital Medical University,Beijing,China5.Laboratory Animal Center,School of Life Sciences,Peking University,Beijing,China6.Institute of Medical Biology,Chinese Academy of Medical Sciences and Peking Union Medical College,Kunming,China7.School of Psychological and Cognitive Sciences,IDG/McGovern Institute for Brain Research,Peking University,Beijing,China
摘要：Background:Quantifying the rich home-cage activities of tree shrews provides a relia-ble basis for understanding their daily routines and building disease models.However,due to the lack of effective behavioral methods,most efforts on tree shrew behavior are limited to simple measures,resulting in the loss of much behavioral information.
Methods:To address this issue,we present a deep learning(DL)approach to achieve markerless pose estimation and recognize multiple spontaneous behaviors of tree shrews,including drinking,eating,resting,and staying in the dark house,etc.
Results:This high-throughput approach can monitor the home-cage activities of 16 tree shrews simultaneously over an extended period.Additionally,we demonstrated an innovative system with reliable apparatus,paradigms,and analysis methods for investigating food grasping behavior.The median duration for each bout of grasping was 0.20s.
Conclusion:This study provides an efficient tool for quantifying and understand tree shrews' natural behaviors.
机标关键词：activitylearninganalysistreedeepestimationhigh-throughputhome-cage
论文发表日期：2025-03-01
在线出版日期：2025-08-15（本平台首次上网日期，不代表文献的发表时间）
页数：10( 896-905 )
英文信息