Integrating XMALab and DeepLabCut for high-throughput XROMM
J. D. Laurence-Chasen, Armita R. Manafzadeh, Nicholas G. Hatsopoulos, Callum F. Ross, Fritzie I. Arce‐McShane
Abstract
Marker tracking is a major bottleneck in studies involving X-ray Reconstruction of Moving Morphology (XROMM). Here, we tested whether DeepLabCut, a new deep learning package built for markerless tracking, could be applied to videoradiographic data to improve data processing throughput. Our novel workflow integrates XMALab, the existing XROMM marker tracking software, and DeepLabCut while retaining each program's utility. XMALab is used for generating training datasets, error correction, and 3D reconstruction, whereas the majority of marker tracking is transferred to DeepLabCut for automatic batch processing. In the two case studies that involved an in vivo behavior, our workflow achieved a 6 to 13-fold increase in data throughput. In the third case study, which involved an acyclic, post mortem manipulation, DeepLabCut struggled to generalize to the range of novel poses and did not surpass the throughput of XMALab alone. Deployed in the proper context, this new workflow facilitates large scale XROMM studies that were previously precluded by software constraints.