Department of Computer Vision & Remote Sensing, Technische Universität Berlin, Berlin, Germany.
Institute of Animal Welfare, Animal Behavior, and Laboratory Animal Science, Department of Veterinary Medicine, Freie Universität Berlin, Berlin, Germany.
PLoS One. 2020 Apr 15;15(4):e0228059. doi: 10.1371/journal.pone.0228059. eCollection 2020.
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 set of images of black-furred laboratory mice that were moving freely is used and provided. Images were obtained after anesthesia (with isoflurane or ketamine/xylazine combination) and surgery (castration). We deploy two pre-trained state of the art deep convolutional neural network (CNN) architectures (ResNet50 and InceptionV3) and compare to a third CNN architecture without pre-training. Depending on the particular treatment, we achieve an accuracy of up to 99% for the recognition of the absence or presence of post-surgical and/or post-anesthetic effects on the facial expression.
评估动物的福利受到人类与动物之间有效沟通的限制。除了直接沟通，还采用了各种参数来评估动物的福利。特别是在生物医学研究领域，由于伦理和法律原因，非常需要科学合理的工具来评估实验动物的疼痛、痛苦和不适。对于老鼠这种最常用的实验动物，一种有价值的工具是老鼠面部表情疼痛编码系统（Mouse Grimace Scale，MGS）。我们旨在开发一种用于监测手术后和麻醉后小鼠影响的全自动系统。我们的工作介绍了一个半自动化的流水线，作为实现这一目标的第一步。使用并提供了一组新的黑色实验鼠自由移动的图像数据集。这些图像是在麻醉（异氟烷或氯胺酮/赛拉嗪联合使用）和手术后（阉割）获得的。我们部署了两个预先训练的最先进的深度卷积神经网络（CNN）架构（ResNet50 和 InceptionV3），并与第三个没有预先训练的 CNN 架构进行了比较。根据特定的治疗方法，我们在识别手术后和/或麻醉后对面部表情的影响的存在或不存在方面的准确率高达 99%。
