Three‐dimensional‐based landmark tracker employing a superpixels method for neuroscience, biomechanics, and biology studies

Abstract

Examining locomotion has improved our basic understanding of motor control and aided in treating motor impairment. Mice and rats are premier models of human disease and increasingly the model systems of choice for basic neuroscience. High frame rates (250 Hz) are needed to quantify the kinematics of these running rodents. Manual tracking, especially for multiple markers, becomes time‐consuming and impossible for large sample sizes. Therefore, the need for automatic segmentation of these markers has grown in recent years. Here, we address this need by presenting a method to segment the markers using the simple linear iterative clustering superpixel method. The two‐dimensional coordinates on the image plane are projected to a three‐dimensional (3D) domain using direct linear transform and a 3D Kalman filter has been used to predict the position of markers based on the speed and position of markers from the previous frames. A probabilistic function is used to find the best match from among superpixels. The method is evaluated for different difficulties for tracking of the markers, and it achieves 95% correct labeling of markers. Finally, the method was used to analyze data from an ongoing study seeking to improve recovery from spinal cord injury in rats

A novel method for robust markerless tracking of rodent paws in 3D

“…Here, we extend these methods to make a more robust, automated tracker for paws (required one-time clicking of user), by utilizing 3D information across views, and temporal prediction based on generalized paw kinematics. The main contribution of this study is thus a markerless paw tracker that is robust to occlusions and collisions over multiple frames, resolving many of the limitations of our previous work [17], [19]. We achieved this using 3D information, integrating information from four cameras at the same time.…”