Automated measurement of stress scores in video recordings of laboratory mice

自动测量实验室小鼠视频记录中的压力评分

• 批准号: 408132301
• 负责人: Professorin Dr.-Ing. Dorit Merhof
• 金额: --
• 依托单位: Professur für Bildverarbeitung
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2018
• 资助国家: 德国
• 起止时间: 2017-12-31 至 2020-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/408132301?language=en
• 关键词: Automated; measurement; stress; scores; video

Using laboratory mice for biomedical research requires strict monitoring of animal wellbeing. Especially, close monitoring of stress and pain is a key requirement for any pre-clinical study incorporating laboratory mice. Numerous protocols for mouse stress assessment have been developed including grimace analysis using the mouse grimace scale (MGS) or analysis of behavioral patterns such as burrowing and nesting behavior that all differ strongly between healthy and non-healthy individuals. While these methods are well established and tools such as MGS show a high correlation between visually acquired score and baseline measurements, assessing animal wellbeing with these scales is a time-consuming task. The animals need to be placed into special environments that are monitored using video equipment and the videos are subsequently analyzed manually. Furthermore, to ensure comparability and to reduce inter-rater variability, it is common practice to have the same expert analyze all videos of a trial.The aforementioned requirements and restrictions result in a high workload and planning overhead for the laboratory personnel. Therefore, this project aims at developing novel methods for automated behavioral analysis and assessment of pain and stress in laboratory mice. The developed algorithms will allow continuous, objective, quantitative and reproducible analysis of acquired video data. The primary focus will be on the development of methods supporting existing imaging setups or those being currently developed in parallel research projects, allowing quick deployment of the developed methods into existing lab environments and retrospective analysis of already existing video data.During the first project stage, a method automatically assessing of the mouse grim scale will be developed to allow robust automated analysis of animal grimacing. In the second stage, we aim at developing methods for robust analysis of behavioral experiments with laboratory mice filmed in open environments. These experiments include analyzing the nesting and burrowing behavior of mice, two experimental setups that are also currently being evaluated manually with considerable manual effort. Two other work packages include extending our methods to home cage analysis for continuous assessment and research on novel image-based and automatically detectable markers for data-driven pain assessment. We aim at developing methods that allow continuous real-time assessment of the animal state with minimal required user interaction, resulting in drastically improved response time, work effort, evaluation quantification and an overall contribution to the animals’ wellbeing.The developed methods will be made publicly available as open source software bundle allowing incorporating them into existing imaging setups.

使用实验室小鼠进行生物医学研究需要严格监控动物的健康状况。特别是，密切监测压力和疼痛是任何临床前研究纳入实验室小鼠的关键要求。许多老鼠压力评估方案已经被开发出来，包括使用老鼠鬼脸量表（MGS）进行鬼脸分析，或分析行为模式，如在健康和非健康个体之间存在强烈差异的挖洞和筑巢行为。虽然这些方法已经建立，MGS等工具显示视觉获得的分数与基线测量值之间存在高度相关性，但用这些量表评估动物的健康状况是一项耗时的任务。这些动物需要被放置在特殊的环境中，使用视频设备进行监控，随后对视频进行人工分析。此外，为了确保可比性并减少评分者之间的差异，通常的做法是让同一位专家分析一次试验的所有视频。上述要求和限制导致实验室人员的高工作量和计划开销。因此，本项目旨在开发用于实验室小鼠疼痛和应激的自动行为分析和评估的新方法。开发的算法将允许对获得的视频数据进行连续、客观、定量和可重复的分析。主要重点将放在支持现有成像设置或目前正在并行研究项目中开发的方法的开发上，允许将开发的方法快速部署到现有的实验室环境中，并对已经存在的视频数据进行回顾性分析。在第一个项目阶段，将开发一种自动评估老鼠表情的方法，以允许对动物表情进行强大的自动分析。在第二阶段，我们的目标是开发在开放环境中拍摄的实验室小鼠行为实验的稳健分析方法。这些实验包括分析老鼠的筑巢和挖洞行为，这两个实验设置目前也正在进行人工评估，需要大量的人工努力。其他两个工作包包括将我们的方法扩展到家庭笼分析，用于连续评估和研究新的基于图像的自动检测标记，用于数据驱动的疼痛评估。我们的目标是开发方法，允许持续实时评估动物状态，最小限度地减少用户交互，从而大大改善响应时间，工作努力，评估量化和对动物福祉的整体贡献。开发的方法将作为开源软件包公开提供，允许将它们合并到现有的成像设置中。