Abstract
Continuous, automated monitoring of laboratory mice enables more accurate data collection and improves animal welfare through real-time insights. Researchers can achieve a more dynamic and clinically relevant characterization of disease progression and therapeutic effects by integrating behavioral and physiological monitoring in the home cage. However, providing individual mouse metrics is difficult because of their housing density, similar appearances, high mobility, and frequent interactions. To address these challenges, we develop a real-time identification (ID) algorithm that accurately assigns ID predictions to mice wearing custom ear tags in digital home cages monitored by cameras. Our pipeline consists of three parts: (1) a custom multiple object tracker (MouseTracks) that combines appearance and motion cues from mice; (2) a transformer-based ID classifier (Mouseformer); and (3) a tracklet associator linear program to assign final ID predictions to tracklets (MouseMap). Our models assign an animal ID based on custom ear tags at 30 frames per second with 24/7 cage coverage. We show that our custom tracking and ID pipeline improves tracking efficiency and lowers ID switches across mouse strains and various environmental factors compared to current mouse tracking methods.
Abstract (translated)
持续的自动化监测实验室小鼠能够更准确地收集数据，并通过实时洞察提高动物福利。研究人员可以通过在笼舍中整合行为和生理监测，实现对疾病进展及治疗效果的动态且具有临床相关性的特征描述。然而，由于小鼠的高密度饲养、相似外观、高度移动性和频繁互动等原因，提供个体小鼠指标变得困难。 为了解决这些挑战，我们开发了一种实时识别（ID）算法，该算法能够准确地将佩戴定制耳标的数字笼舍中由摄像头监控的小鼠进行身份预测。我们的管道包括三个部分： 1. 一个结合了小鼠外观和运动线索的定制多对象跟踪器(MouseTracks)； 2. 基于变压器的身份分类器(Mouseformer)； 3. 将最终ID预测分配给轨迹片段(tracklet)的关联程序(MouseMap)。 我们的模型能够在每秒30帧的速度下，根据定制耳标为动物分配一个ID，并实现全天候笼舍覆盖。我们展示了与当前小鼠跟踪方法相比，我们的自定义追踪和ID管道在不同品系的小鼠以及各种环境因素下提高了追踪效率并降低了身份切换率。
URL
https://arxiv.org/abs/2507.07929
