Application of Superpixels to Segment Several Landmarks in Running Rodents
This work addresses the time-consuming manual tracking in locomotion studies for neuroscience research, but it is incremental as it applies existing superpixel techniques to a specific domain.
The authors tackled the problem of manually tracking multiple body landmarks in high-frame-rate videos of running rodents by proposing a superpixel-based image segmentation method, which achieved slightly better segmentation in RGB compared to hue but better merging and classification in hue.
Examining locomotion has improved our basic understanding of motor control and aided in treating motor impairment. Mice and rats are the model system of choice for basic neuroscience studies of human disease. High frame rates are needed to quantify the kinematics of running rodents, due to their high stride frequency. Manual tracking, especially for multiple body landmarks, becomes extremely time-consuming. To overcome these limitations, we proposed the use of superpixels based image segmentation as superpixels utilized both spatial and color information for segmentation. We segmented some parts of body and tested the success of segmentation as a function of color space and SLIC segment size. We used a simple merging function to connect the segmented regions considered as neighbor and having the same intensity value range. In addition, 28 features were extracted, and t-SNE was used to demonstrate how much the methods are capable to differentiate the regions. Finally, we compared the segmented regions to a manually outlined region. The results showed for segmentation, using the RGB image was slightly better compared to the hue channel. For merg- ing and classification, however, the hue representation was better as it captures the relevant color information in a single channel.