Biplanar Videoradiography Dataset for Model-based Pose Estimation Development and New User Training.

Measuring the motion of the small foot bones is critical for understanding pathological loss of function. Biplanar videoradiography is well-suited to measure in vivo bone motion, but challenges arise when estimating the rotation and translation (pose) of each bone. The bone's pose is typically estimated with marker- or model-based methods. Marker-based methods are highly accurate but uncommon in vivo due to their invasiveness. Model-based methods are more common but are currently less accurate as they rely on user input and lab-specific algorithms. This work presents a rare in vivo dataset of the calcaneus, talus, and tibia poses, as measured with marker-based methods during running and hopping. A method is included to train users to improve their initial guesses into model-based pose estimation software, using marker-based visual feedback. New operators were able to estimate bone poses within 2° of rotation and 1 mm of translation of the marker-based pose, similar to an expert user of the model-based software, and representing a substantial improvement over previously reported inter-operator variability. Further, this dataset can be used to validate other model-based pose estimation software. Ultimately, sharing this dataset will improve the speed and accuracy with which users can measure bone poses from biplanar videoradiography.