refineDLC: An advanced post-processing pipeline for DeepLabCut outputs

DeepLabCut has transformed behavioral and locomotor research by enabling markerless pose estimation through deep learning. Despite its broad adoption across species and behaviors, quantitative kinematic analyses remained limited by noisy outputs and the computational expertise required for refinement. To address this issue, we introduce refineDLC, a comprehensive post-processing pipeline that streamlines the conversion of noisy DeepLabCut outputs into robust, analytically reliable kinematic data. The pipeline incorporates essential cleaning steps, including inversion of the y-coordinates for intuitive spatial interpretation, removal of zero-value frames, and exclusion of irrelevant body part labels. It further applies dual-stage filtering based on likelihood scores and positional changes, enhancing data accuracy and consistency. Multiple interpolation strategies manage missing values while maintaining data continuity and integrity. We evaluated refineDLC using two datasets: controlled locomotion in cattle and field-recorded trotting horses. Across both contexts, the pipeline substantially improved data quality and interpretability, reducing variability, eliminating false-positive labeling errors, and transforming noisy trajectories into physiologically meaningful kinematic patterns. Outputs were reliable and analysis-ready regardless of recording conditions or species. By simplifying the transformation from raw DeepLabCut outputs to meaningful kinematic insights, refineDLC expands accessibility for researchers, particularly those with limited programming expertise, enabling precise quantitative analyses at scale. Future developments may incorporate adaptive filtering algorithms and real-time quality assessments, further optimizing performance and automation. These enhancements will extend the pipeline's applicability to precision phenotyping, behavioral ecology, animal science, and conservation biology.