Markerless analysis of hindlimb kinematics in spinal cord-injured mice through deep learning.

Rodent models are commonly used to understand the underlying mechanisms of spinal cord injury (SCI). Kinematic analysis, an important technique to measure dysfunction of locomotion after SCI, is generally based on the capture of physical markers placed on bony landmarks. However, marker-based studies face significant experimental hurdles such as labor-intensive manual joint tracking, alteration of natural gait by markers, and skin error from soft tissue movement on the knee joint. Although the pose estimation strategy using deep neural networks can solve some of these issues, it remains unclear whether this method is adaptive to SCI mice with abnormal gait. In the present study, we developed a deep learning based markerless method of 2D kinematic analysis to automatically track joint positions. We found that a relatively small number (< 200) of manually labeled video frames was sufficient to train the network to extract trajectories. The mean test error was on average 3.43 pixels in intact mice and 3.95 pixels in SCI mice, which is comparable to the manual tracking error (3.15 pixels, less than 1 mm). Thereafter, we extracted 30 gait kinematic parameters and found that certain parameters such as step height and maximal hip joint amplitude distinguished intact and SCI locomotion.