Segmentation tracking and clustering system enables accurate multi-animal tracking of social behaviors
Year: 2024
Type: article
Abstract: Accurate analysis of social behaviors in animals is hindered by methodological challenges. Here, we develop a segmentation tracking and clustering system (STCS) to address two major challenges in computational neuroethology: reliable multi-animal tracking and pose estimation under complex interaction conditions and providing interpretable insights into social differences guided by genotype information. We established a comprehensive, long-term, multi-animal-tracking dataset across various experimental settings. Benchmarking STCS against state-of-the-art tracking algorithms, we demonstrated its superior efficacy in analyzing behavioral experiments and establishing a robust tracking baseline. By analyzing the behavior of mice with autism spectrum disorder (ASD) using a novel weakly supervised clustering method under both solitary and social conditions, STCS reveals potential links between social stress and motor impairments. Benefiting from its modular and web-based design, STCS allows researchers to easily integrate the latest computer vision methods, enabling comprehensive behavior analysis services over the Internet, even from a single laptop.
Source: Patterns
Authors: Cheng Tang, Yang Zhou, Shuaizhu Zhao, Mingshu Xie, Ruizhe Zhang, Xiaoyan Long, Lingqiang Zhu, Youming Lu, Guangzhi Ma, Hao Li
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $4,900
OpenAlex ID: W4402407785
