FIM Imaging and FIMtrack: Two New Tools Allowing High-throughput and Cost Effective Locomotion Analysis

The analysis of neuronal network function requires a reliable measurement of behavioral traits. Since the behavior of freely moving animals is variable to a certain degree, many animals have to be analyzed, to obtain statistically significant data. This in turn requires a computer assisted automated quantification of locomotion patterns. To obtain high contrast images of almost translucent and small moving objects, a novel imaging technique based on frustrated total internal reflection called FIM was developed. In this setup, animals are only illuminated with infrared light at the very specific position of contact with the underlying crawling surface. This methodology results in very high contrast images. Subsequently, these high contrast images are processed using established contour tracking algorithms. Based on this, we developed the FIMTrack software, which serves to extract a number of features needed to quantitatively describe a large variety of locomotion characteristics. During the development of this software package, we focused our efforts on an open source architecture allowing the easy addition of further modules. The program operates platform independent and is accompanied by an intuitive GUI guiding the user through data analysis. All locomotion parameter values are given in form of csv files allowing further data analyses. In addition, a Results Viewer integrated into the tracking software provides the opportunity to interactively review and adjust the output, as might be needed during stimulus integration. The power of FIM and FIMTrack is demonstrated by studying the locomotion of Drosophila larvae

Comparison of two 3D tracking paradigms for freely flying insects

In this paper, we discuss and compare state-of-the-art 3D tracking paradigms for flying insects such as Drosophila melanogaster. If two cameras are employed to estimate the trajectories of these identical appearing objects, calculating stereo and temporal correspondences leads to an NP-hard assignment problem. Currently, there are two different types of approaches discussed in the literature: probabilistic approaches and global correspondence selection approaches. Both have advantages and limitations in terms of accuracy and complexity. Here, we present algorithms for both paradigms. The probabilistic approach utilizes the Kalman filter for temporal tracking. The correspondence selection approach calculates the trajectories based on an overall cost function. Limitations of both approaches are addressed by integrating a third camera to verify consistency of the stereo pairings and to reduce the complexity of the global selection. Furthermore, a novel greedy optimization scheme is introduced for the correspondence selection approach. We compare both paradigms based on synthetic data with ground truth availability. Results show that the global selection is more accurate, while the previously proposed tracking-by-matching (probabilistic) approach is causal and feasible for longer tracking periods and very high target densities. We further demonstrate that our extended global selection scheme outperforms current correspondence selection approaches in tracking accuracy and tracking time

FIMTrack: An open source tracking and locomotion analysis software for small animals

<div><p>Imaging and analyzing the locomotion behavior of small animals such as Drosophila larvae or C. elegans worms has become an integral subject of biological research. In the past we have introduced FIM, a novel imaging system feasible to extract high contrast images. This system in combination with the associated tracking software FIMTrack is already used by many groups all over the world. However, so far there has not been an in-depth discussion of the technical aspects. Here we elaborate on the implementation details of FIMTrack and give an in-depth explanation of the used algorithms. Among others, the software offers several tracking strategies to cover a wide range of different model organisms, locomotion types, and camera properties. Furthermore, the software facilitates stimuli-based analysis in combination with built-in manual tracking and correction functionalities. All features are integrated in an easy-to-use graphical user interface. To demonstrate the potential of FIMTrack we provide an evaluation of its accuracy using manually labeled data. The source code is available under the GNU GPLv3 at <a href="https://github.com/i-git/FIMTrack" target="_blank">https://github.com/i-git/FIMTrack</a> and pre-compiled binaries for Windows and Mac are available at <a href="http://fim.uni-muenster.de" target="_blank">http://fim.uni-muenster.de</a>.</p></div

Deviations for the examined parameters.

<p>Body bending is given in degree, all other parameters are given in pixels. Max^⋆ represents the values obtained by including outliers whereas OL gives the number of outliers.</p

Calculation of the animal representation.

<p>(<b>A</b>) Example of the sliding window algorithm for points and with a window size of 5. Contour points with sharp angles are given in red. (<b>B</b>) Animal representation including the notation given in the text. (<b>C</b>) Body bending is calculated based on <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005530#pcbi.1005530.e056" target="_blank">Eq (3)</a>. An animal is not bended if <i>γ</i> = 180°, bended to the left if <i>γ</i> > 180° and bended to the right if <i>γ</i> < 180°.</p

Measured deviations.

<p>(<b>A</b>) Center of mass deviations. (<b>B</b>) Central spine point deviations. (<b>C</b>) Body bending angle deviations. The mean divergence of the body bending is sketched by the light yellow area in the larva image at the top left corner. (<b>D</b>) The coiled structure of larva 6 (at <i>t</i> = 2, 3) causes outliers in the measurements (compare to <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005530#pcbi.1005530.t001" target="_blank">Table 1</a>). The head is given in red and tail is given in blue.</p