High-throughput markerless pose estimation and home-cage activity analysis of tree shrew using deep learning.

Journal: Animal models and experimental medicine
Published Date: Jan 23, 2025

Abstract

BACKGROUND: Quantifying the rich home-cage activities of tree shrews provides a reliable 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.

Authors

  • Yangzhen Wang
    State Key Laboratory of Membrane Biology, School of Life Sciences, Beijing, 100871, China.
  • Feng Su
    Robotics Institute, Beihang University, Beijing, 100191, China.
  • Rixu Cong
    Ministry of Education, Key Laboratory of Cell Proliferation and Differentiation, College of Life Sciences, Peking University, Beijing, China.
  • Mengna Liu
    School of Basic Medical Sciences, Beijing Key Laboratory of Neural Regeneration and Repair, Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China.
  • Kaichen Shan
    Department of Automation, Tsinghua University, Beijing, China.
  • Xiaying Li
    Laboratory Animal Center, School of Life Sciences, Peking University, Beijing, China.
  • Desheng Zhu
    Laboratory Animal Center, School of Life Sciences, Peking University, Beijing, China.
  • Yusheng Wei
    Laboratory Animal Center, School of Life Sciences, Peking University, Beijing, China.
  • Jiejie Dai
    Institute of Medical Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Kunming, China.
  • Chen Zhang
    Department of Dermatology, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing, China.
  • Yonglu Tian
    School of Basic Medical Sciences, Beijing Key Laboratory of Neural Regeneration and Repair, Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China.

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