Unger Jakob, Mansour Mike, Kopaczka Marcin, Gronloh Nina, Spehr Marc, Merhof Dorit
Institute of Imaging and Computer Vision, RWTH Aachen University, Kopernikusstr. 16, Aachen, 52056, Germany.
Department of Chemosensation, Institute of Biology II, RWTH Aachen University, Worringer Weg 3, Aachen, 52074, Germany.
BMC Bioinformatics. 2017 May 25;18(1):272. doi: 10.1186/s12859-017-1681-1.
In neuroscience research, mouse models are valuable tools to understand the genetic mechanisms that advance evidence-based discovery. In this context, large-scale studies emphasize the need for automated high-throughput systems providing a reproducible behavioral assessment of mutant mice with only a minimum level of manual intervention. Basic element of such systems is a robust tracking algorithm. However, common tracking algorithms are either limited by too specific model assumptions or have to be trained in an elaborate preprocessing step, which drastically limits their applicability for behavioral analysis.
We present an unsupervised learning procedure that is basically built as a two-stage process to track mice in an enclosed area using shape matching and deformable segmentation models. The system is validated by comparing the tracking results with previously manually labeled landmarks in three setups with different environment, contrast and lighting conditions. Furthermore, we demonstrate that the system is able to automatically detect non-social and social behavior of interacting mice. The system demonstrates a high level of tracking accuracy and clearly outperforms the MiceProfiler, a recently proposed tracking software, which serves as benchmark for our experiments.
The proposed method shows promising potential to automate behavioral screening of mice and other animals. Therefore, it could substantially increase the experimental throughput in behavioral assessment automation.
在神经科学研究中，小鼠模型是理解推进循证发现的遗传机制的宝贵工具。在此背景下，大规模研究强调需要自动化高通量系统，以仅在最低程度的人工干预下对突变小鼠进行可重复的行为评估。此类系统的基本要素是强大的跟踪算法。然而，常见的跟踪算法要么受限于过于特定的模型假设，要么必须在精心的预处理步骤中进行训练，这极大地限制了它们在行为分析中的适用性。
我们提出了一种无监督学习程序，该程序基本上构建为一个两阶段过程，使用形状匹配和可变形分割模型在封闭区域内跟踪小鼠。通过在三种具有不同环境、对比度和光照条件的设置中，将跟踪结果与先前手动标记的地标进行比较，对该系统进行了验证。此外，我们证明该系统能够自动检测相互作用小鼠的非社交和社交行为。该系统展示了高水平的跟踪准确性，明显优于最近提出的跟踪软件MiceProfiler，该软件作为我们实验的基准。
所提出的方法显示出在自动化小鼠和其他动物行为筛选方面的潜在前景。因此，它可以大幅提高行为评估自动化中的实验通量。
