基于深度学习的树鼩高通量无标记姿态估计及笼内活动分析
High-throughput markerless pose estimation and home-cage activity analysis of tree shrew using deep learning.
作者信息
Wang Yangzhen, Su Feng, Cong Rixu, Liu Mengna, Shan Kaichen, Li Xiaying, Zhu Desheng, Wei Yusheng, Dai Jiejie, Zhang Chen, Tian Yonglu
机构信息
Department of Automation, Tsinghua University, Beijing, China.
College of Future Technology, Peking University, Beijing, China.
出版信息
Animal Model Exp Med. 2025 May;8(5):896-905. doi: 10.1002/ame2.12530. Epub 2025 Jan 23.
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.
背景
量化树鼩丰富的笼内活动为了解它们的日常行为和建立疾病模型提供了可靠的基础。然而，由于缺乏有效的行为学方法，大多数对树鼩行为的研究仅限于简单的测量，导致大量行为信息丢失。
方法
为了解决这个问题，我们提出了一种深度学习（DL）方法，以实现无标记姿态估计并识别树鼩的多种自发行为，包括饮水、进食、休息和待在暗室等。
结果
这种高通量方法可以在较长时间内同时监测16只树鼩的笼内活动。此外，我们展示了一个具有可靠设备、范式和分析方法的创新系统，用于研究食物抓取行为。每次抓取行为的中位持续时间为0.20秒。
结论
本研究为量化和理解树鼩的自然行为提供了一种有效的工具。
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