IEEE/ACM Trans Comput Biol Bioinform. 2019 Mar-Apr;16(2):465-474. doi: 10.1109/TCBB.2017.2779141. Epub 2017 Dec 4.
Drosophila melanogaster is an important model organism for ongoing research in neuro- and behavioral biology. Especially the locomotion analysis has become an integral part of such studies and thus elaborated automated tracking systems have been proposed in the past. However, most of these approaches share the inability to precisely segment the contours of colliding animals leading to the absence of model and motion-related features during collisions. Here, we translate the task of tracking and resolving colliding animals into a filtering problem solvable by Markov Chain Monte Carlo methods and elaborate an adequate larva model. By comparing our method with state-of-the-art approaches, we demonstrate that our algorithm produces significantly better results in a fraction of time and facilitates the analysis of animal behavior during interaction in more detail.
黑腹果蝇是神经科学和行为生物学研究中的重要模式生物。特别是运动分析已成为此类研究的一个组成部分，因此过去提出了许多自动跟踪系统。然而，这些方法大多都无法准确地分割碰撞动物的轮廓，导致在碰撞过程中缺乏模型和运动相关的特征。在这里，我们将跟踪和解决碰撞动物的任务转化为一个可以通过马尔可夫链蒙特卡罗方法解决的滤波问题，并详细阐述了一个合适的幼虫模型。通过将我们的方法与最先进的方法进行比较，我们证明了我们的算法在更短的时间内产生了显著更好的结果，并更详细地促进了动物在相互作用过程中的行为分析。
