Towards an open-source, equipment-agnostic framework for automated welfare monitoring in the home cage

The moral implications of using mice and other higher animals in biomedical research has been a topic of debate for many years and has steered various efforts towards minimising animal suffering. As the number of mice used in biomedical research continues to rise, there is greater emphasis than ever on approaches to refine husbandry protocols and humane endpoints to ensure that welfare concerns are dealt with swiftly and efficiently.

Home cage monitoring has been proposed as a means capturing animal behaviour through the light and dark phases so that welfare deficits might be captured without the need for human intervention. Several commercial and non-commercial systems have been designed to capture video footage (and other data) of mice that offer several advantages over conventional experimental designs:

1) Behaviours can be captured in a familiar, enriched environment

2) Observations are not confounded by novel apparatus or human experimenters

3) It serves as a permanent digital record

4) Video data are amenable to automated image analysis techniques

The challenge that remains in the context of home cage analysis (HCA) is the ability to analyse video footage in a manner that is fully-automated, comprehensive, robust, and computationally efficient. Under this proposal, the successful studentship awardee will develop a solution to the problem of automated HCA based on deep learning; a state-of-the-art approach to computer vision problems that utilises computational models inspired by the human visual cortex. The technique will be based on the "anomaly detection", whereby a model is initially trained to capture the range of possible behaviours exhibited by mice under normal circumstances (i.e., without welfare deficits). The methods works by learning to produce a set of visual and motion features that describe the behaviours of the mice, and then having the same model attempt to "reconstruct" the input clip from the features. In doing so, the model is forced to capture only key information about normal behaviour. Given that video clips depicting welfare deficits are visually different normal behaviours by definition, the model will be less capable of representing those data.

Dr James Brown will be the primary supervisor of this work, who has a strong record of interdisciplinary research involving mouse models. Dr Brown has previously worked with the Mary Lyon Centre - a collaborator on this project - on development 3D image analysis techniques for mouse embryo phenotyping. Dr Brown has recently published several high-impact articles on machine learning techniques for the diagnosis of retinal disease, and is currently seeking approval from the US Food & Drug Administration.

Prof Xujiong Ye will co-supervise this project, bringing more than 20 years' experience in computer vision and medical image analysis in both industry and academia. The methods outlined in this proposal align well recent work published Prof Ye on the development of algorithms to track and assess the welfare of farmyard pigs from top-down video footage.

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PhD Studentship



Principal investigator

Dr James Brown


University of Lincoln


Professor Xujiong Ye

Grant reference number


Award date:

Oct 2020 - Sep 2023

Grant amount