The Mouse Action Recognition System (MARS) software pipeline for automated analysis of social behaviors in mice.
The Mouse Action Recognition System (MARS) software pipeline for automated analysis of social behaviors in mice.
复制标题
小鼠动作识别系统（MARS）软件管道，用于自动分析小鼠的社会行为。
DOI：
10.7554/elife.63720
复制
发表时间：
2021-11-30
期刊：
影响因子：
7.7
通讯作者：
Kennedy A
中科院分区：
文献类型：
--
作者：
Segalin C;Williams J;Karigo T;Hui M;Zelikowsky M;Sun JJ;Perona P;Anderson DJ;Kennedy A
自然主义社会行为的研究需要量化动物的相互作用。这通常是通过手动注释完成的，这是一个非常耗时和繁琐的过程。计算机视觉的最新进展使得能够跟踪自由行为动物的姿势（姿势）。然而，自动和准确地分类复杂的社会行为仍然具有技术挑战性。我们介绍了鼠标动作识别系统（MARS），一个自动化的管道姿态估计和行为量化对自由互动的小鼠。我们将MARS的注释与人类注释进行比较，发现MARS的姿态估计和行为分类达到了人类水平的性能。我们还发布了用于训练MARS的姿势和注释数据集，作为社区基准和资源。最后，我们介绍了行为环境和神经轨迹观测台（BENTO），一个图形用户界面，用于分析多模态神经科学数据集。MARS和BENTO一起提供了一个端到端的管道，用于在一个用户友好且易于修改的包中进行行为数据提取和分析。
The study of naturalistic social behavior requires quantification of animals’ interactions. This is generally done through manual annotation—a highly time-consuming and tedious process. Recent advances in computer vision enable tracking the pose (posture) of freely behaving animals. However, automatically and accurately classifying complex social behaviors remains technically challenging. We introduce the Mouse Action Recognition System (MARS), an automated pipeline for pose estimation and behavior quantification in pairs of freely interacting mice. We compare MARS’s annotations to human annotations and find that MARS’s pose estimation and behavior classification achieve human-level performance. We also release the pose and annotation datasets used to train MARS to serve as community benchmarks and resources. Finally, we introduce the Behavior Ensemble and Neural Trajectory Observatory (BENTO), a graphical user interface for analysis of multimodal neuroscience datasets. Together, MARS and BENTO provide an end-to-end pipeline for behavior data extraction and analysis in a package that is user-friendly and easily modifiable.