FlyLimbTracker: an active contour based approach for leg segment tracking in unmarked, freely behaving Drosophila

Abstract

Understanding the biological underpinnings of movement and action requires the development of tools for precise, quantitative, and high-throughput 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 Drosophila. We show that this approach is robust to wide variations in video spatial and temporal resolution and that it can be used to measure leg segment motions during 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.\n\nAuthor SummaryIn terrestrial animals, including humans, fundamental actions like locomotion and grooming emerge from the displacement of multiple limbs through space. Therefore, precise measurements of limb movements are critical for investigating and, ultimately, understanding the neural basis for behavior. The vinegar fly, Drosophila melanogaster, is an attractive animal model for uncovering general principles about limb control since its genome and nervous system are easy to manipulate. However, existing methods for measuring leg movements in freely behaving Drosophila have significant drawbacks: they require complicated experimental setups and provide limited information about each leg. Here we report a new method - and provide its open-source software implementation, FlyLimbTracker - for tracking the body and leg segments of freely behaving flies using only computational image processing approaches. We illustrate the power of this method by tracking fly limbs during five distinct walking and grooming behaviors and from videos across a wide range of spatial and temporal resolutions. Our approach is generalizable, allowing researchers to use and customize our software for limb tracking in Drosophila and in other species.