Robust detection and tracking of laboratory animals in large-scale home-cage video recordings

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Animal experiments are still an indispensable component in many areas of research. The current EU directive on this (Directive 2010/63/EU) is based on the 3Rs principle and aims to replace, reduce, and refine animal testing wherever possible. In addition to regular observation and assessment of the health status of laboratory animals by trained personnel, continuously recorded video data provide a rich and objective source of information for analyzing the behavior and stress perception of laboratory animals. Although direct quantitative analysis of video data is nearly impossible and too time-consuming for researchers, automatic localization of laboratory animals, automatic detection of anatomical landmarks, and analysis of animal movement trajectories using state-of-the-art computer vision algorithms are becoming increasingly accurate and allow detailed analyses of social and individual behavior. Tracking of laboratory animals in video data can be performed non-invasively, allowing detailed behavioral studies without possible bias caused by human interference. The home-cage developed within FOR represents such a non-invasive environment. However, the quality of the quantifications obtained depends significantly on the robustness of the detection and tracking algorithms, i.e., the animals must be tracked without error over longer periods of time. Although video recording and automated archiving have already been established in the previous phases of the project, the processing and analysis of the recorded video data is a remaining challenge that we will address in this project. Typical experiment durations are in the order of several days of continuous video recording with several terabytes of video material per experiment, which must be archived and analyzed fully automatically. The proposed subproject nP20 within FOR aims to improve and extend the final parts of this home-cage analysis pipeline. It includes (1) an improved strategy for annotating training data, (2) parallelizable algorithms for reliable detection and segmentation of laboratory animals in top-view images, (3) a robust tracking algorithm for temporal assignment of detected objects, and (4) a set of quantitative descriptors for analyzing the (social) behavior of animals based on the extracted tracking data. All developed methods will be quantitatively validated and finally used to analyze video data from home-cage experiments collected in the first two phases of FOR (5). All implementations will be made publicly available to the scientific community as open-source software (6).