High‐throughput markerless pose estimation and home‐cage activity analysis of tree shrew using deep learning
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, res...
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| Format: | Article |
| Language: | English |
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Wiley
2025-05-01
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| Series: | Animal Models and Experimental Medicine |
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| Online Access: | https://doi.org/10.1002/ame2.12530 |
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| author | Yangzhen Wang Feng Su Rixu Cong Mengna Liu Kaichen Shan Xiaying Li Desheng Zhu Yusheng Wei Jiejie Dai Chen Zhang Yonglu Tian |
| author_facet | Yangzhen Wang Feng Su Rixu Cong Mengna Liu Kaichen Shan Xiaying Li Desheng Zhu Yusheng Wei Jiejie Dai Chen Zhang Yonglu Tian |
| author_sort | Yangzhen Wang |
| collection | DOAJ |
| description | 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. 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 0.20 s. Conclusion This study provides an efficient tool for quantifying and understand tree shrews' natural behaviors |
| format | Article |
| id | doaj-art-a89477b3b5ac49448363660438b953a6 |
| institution | DOAJ |
| issn | 2576-2095 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Wiley |
| record_format | Article |
| series | Animal Models and Experimental Medicine |
| spelling | doaj-art-a89477b3b5ac49448363660438b953a62025-08-20T03:12:31ZengWileyAnimal Models and Experimental Medicine2576-20952025-05-018589690510.1002/ame2.12530High‐throughput markerless pose estimation and home‐cage activity analysis of tree shrew using deep learningYangzhen Wang0Feng Su1Rixu Cong2Mengna Liu3Kaichen Shan4Xiaying Li5Desheng Zhu6Yusheng Wei7Jiejie Dai8Chen Zhang9Yonglu Tian10Department of Automation Tsinghua University Beijing ChinaCollege of Future Technology Peking University Beijing ChinaMinistry of Education, Key Laboratory of Cell Proliferation and Differentiation College of Life Sciences, Peking University Beijing ChinaSchool of Basic Medical Sciences Beijing Key Laboratory of Neural Regeneration and Repair, Advanced Innovation Center for Human Brain Protection, Capital Medical University Beijing ChinaDepartment of Automation Tsinghua University Beijing ChinaLaboratory Animal Center School of Life Sciences, Peking University Beijing ChinaLaboratory Animal Center School of Life Sciences, Peking University Beijing ChinaLaboratory Animal Center School of Life Sciences, Peking University Beijing ChinaInstitute of Medical Biology Chinese Academy of Medical Sciences and Peking Union Medical College Kunming ChinaSchool of Basic Medical Sciences Beijing Key Laboratory of Neural Regeneration and Repair, Advanced Innovation Center for Human Brain Protection, Capital Medical University Beijing ChinaSchool of Psychological and Cognitive Sciences IDG/McGovern Institute for Brain Research, Peking University Beijing ChinaAbstract 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. 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 0.20 s. Conclusion This study provides an efficient tool for quantifying and understand tree shrews' natural behaviorshttps://doi.org/10.1002/ame2.12530deep learningfood graspinghome‐cage activitypose estimationtree shrew |
| spellingShingle | Yangzhen Wang Feng Su Rixu Cong Mengna Liu Kaichen Shan Xiaying Li Desheng Zhu Yusheng Wei Jiejie Dai Chen Zhang Yonglu Tian High‐throughput markerless pose estimation and home‐cage activity analysis of tree shrew using deep learning Animal Models and Experimental Medicine deep learning food grasping home‐cage activity pose estimation tree shrew |
| title | High‐throughput markerless pose estimation and home‐cage activity analysis of tree shrew using deep learning |
| title_full | High‐throughput markerless pose estimation and home‐cage activity analysis of tree shrew using deep learning |
| title_fullStr | High‐throughput markerless pose estimation and home‐cage activity analysis of tree shrew using deep learning |
| title_full_unstemmed | High‐throughput markerless pose estimation and home‐cage activity analysis of tree shrew using deep learning |
| title_short | High‐throughput markerless pose estimation and home‐cage activity analysis of tree shrew using deep learning |
| title_sort | high throughput markerless pose estimation and home cage activity analysis of tree shrew using deep learning |
| topic | deep learning food grasping home‐cage activity pose estimation tree shrew |
| url | https://doi.org/10.1002/ame2.12530 |
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