Automated measurement of stress scores in video recordings of laboratory animals

在第一个FOR 2591资助期间，参与小组分析了一些方法，用于客观评估动物实验中作为应激标志物的适用性。FOR 2591已经发现，基于图像和行为的分数，如鼠标鬼脸量表（MGS），与实际的痛苦有很高的相关性。传统上，这些分数是手动获得的，这导致高手动工作量和潜在的偏差。为了加速和简化这种评估，成像和计算机视觉研究所一直在研究DFG资助的项目“实验室小鼠视频记录中压力分数的自动测量”，该项目与FOR 2591相关。该项目的目标，证明了自动化方法的一般能力，以评估基于图像的严重性分数已成功达到。我们能够证明这个过程可以自动化，并可以导致兽医工作人员所需的评分时间显著减少。在即将到来的资助期内，我们的目标是利用正在进行的项目中获得的见解，开发和验证一个长期监测鼠笼中小鼠和大鼠的系统。将使用2D和3D摄像机的组合对动物进行永久监测，并采集行为参数。通过与FOR 2591合作伙伴的合作，我们将开发一种记录设置，该设置扩展了实验室动物科学中常用的家庭笼类型，并且可以毫不费力地集成到现有的实验室环境中。随后，我们将开发用于动物定位、识别和分析的算法，以评估群体饲养中个体动物的状态。为此，我们将分析运动信息，如行进距离和位置偏好，以及个体行为，如饲养或自我梳理和社会行为，如跟随或避免其他群体成员。将对项目伙伴进行的动物实验导致的这些行为的变化进行分析，以揭示干预措施与行为变化之间的统计相关性。我们将利用现有的图像处理方法以及人工智能和神经网络的最新进展。这些方法将与FOR 2591中选定的合作伙伴一起进行评价，随后与在多中心研究中使用小鼠和大鼠的所有组一起进行验证。这样，我们将评估所开发的方法的普遍性。

During the first FOR 2591 funding period, the participating groups have analysed a number of methods for objective assessment of severity in animal experiments regarding their suitability as stress markers. The FOR 2591 has found out that image- and behaviour-based scores such as the mouse grimace scale (MGS) show a high correlation with the actual distress. Traditionally, these scores are taken manually, which results in high manual workload and potential bias. To speed up and simplify this assessment, the Institute of Imaging and Computer Vision has been working on the DFG-funded project “Automated measurement of stress scores in video recordings of laboratory mice” , which has been associated with the FOR 2591. The project goal of proving the general capability of automated methods for assessing image-based severity scores has been successfully reached. We were able to demonstrate that this process can be automated and can lead to a measurable reduction in scoring time required by veterinary staff.In the upcoming funding period, we aim at using the insights gained during the ongoing project to develop and validate a system for long-term monitoring of mice and rats in their home cages. The animals will be permanently monitored utilizing a combination of 2D and 3D cameras and behaviour parameters will be acquired. In cooperation with FOR 2591 partners, we will develop a recording setup that extends home cage types that are commonly used in laboratory animal science and can be effortlessly integrated into existing lab environments. Subsequently, we will develop algorithms for localization, identification and analysis of animals to allow assessment of the status of individual animals in group housings. To this end, we will analyse locomotor information such as travelled distance and location preference as well as individual behaviour such as rearing or self-grooming and social behaviour such as following or avoiding other group members. Changes to these behaviours as a consequence of animal experiments conducted by the project partners will be analysed to uncover statistical correlations between interventions and the resulting changes in behaviour. We will utilize established image processing methods as well as current advances from artificial intelligence and neural networks. The methods will be evaluated with selected partners from the FOR 2591 and subsequently validated together with all groups who work with mice and rats in multicentre studies. This way, we will assess the generalizability of the developed methods.