Uhlmann Virginie, Ramdya Pavan, Delgado-Gonzalo Ricard, Benton Richard, Unser Michael
Biomedical Imaging Group, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
Institute of Microengineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
PLoS One. 2017 Apr 28;12(4):e0173433. doi: 10.1371/journal.pone.0173433. eCollection 2017.
Understanding the biological underpinnings of movement and action requires the development of tools for quantitative measurements of animal behavior. Drosophila melanogaster provides an ideal model for developing such tools: the fly has unparalleled genetic accessibility and depends on a relatively compact nervous system to generate sophisticated limbed behaviors including walking, reaching, grooming, courtship, and boxing. Here we describe a method that uses active contours to semi-automatically track body and leg segments from video image sequences of unmarked, freely behaving D. melanogaster. We show that this approach yields a more than 6-fold reduction in user intervention when compared with fully manual annotation and can be used to annotate videos with low spatial or temporal resolution for a variety of locomotor and grooming behaviors. FlyLimbTracker, the software implementation of this method, is open-source and our approach is generalizable. This opens up the possibility of tracking leg movements in other species by modifications of underlying active contour models.
理解运动和行为的生物学基础需要开发用于定量测量动物行为的工具。黑腹果蝇为开发此类工具提供了理想模型:果蝇具有无与伦比的遗传易操作性,并且依靠相对紧凑的神经系统来产生复杂的有肢体行为,包括行走、伸展、梳理、求偶和搏斗。在此,我们描述了一种方法,该方法使用活动轮廓从无标记、自由活动的黑腹果蝇的视频图像序列中半自动跟踪身体和腿部节段。我们表明,与完全手动注释相比,这种方法可将用户干预减少6倍以上,并且可用于为各种运动和梳理行为注释低空间或时间分辨率的视频。该方法的软件实现FlyLimbTracker是开源的,我们的方法具有通用性。这为通过修改底层活动轮廓模型来跟踪其他物种的腿部运动开辟了可能性。