Towards a fully automated surveillance of well-being status in laboratory mice using deep learning: Starting with facial expression analysis.

ABSTRACT: Assessing the well-being of an animal is hindered by the limitations of efficient communication between humans and animals. Instead of direct communication, a variety of parameters are employed to evaluate the well-being of an animal. Especially in the field of biomedical research, scientifically sound tools to assess pain, suffering, and distress for experimental animals are highly demanded due to ethical and legal reasons. For mice, the most commonly used laboratory animals, a valuable tool is the Mouse Grimace Scale (MGS), a coding system for facial expressions of pain in mice. We aim to develop a fully automated system for the surveillance of post-surgical and post-anesthetic effects in mice. Our work introduces a semi-automated pipeline as a first step towards this goal. A new data se

SUBMITTER: Andresen N 

PROVIDER: S-EPMC7159220 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature