refineDLC: an advanced post-processing pipeline for DeepLabCut outputs

Abstract

DeepLabCut has notably transformed behavioral and locomotor research by enabling precise, markerless pose estimation through advanced deep learning methods. Despite broad adoption of this approach across species and behaviors, current usage frequently overlooks the challenges of quantitative kinematic analyses due to the complexity and noise within raw model outputs. Researchers are often challenged by the computational expertise required to clean, refine, and interpret the generated data effectively. Here, we introduce refineDLC, a comprehensive post-processing pipeline explicitly designed to streamline the conversion of noisy DeepLabCut outputs into robust, analytically reliable kinematic data. Our user-friendly pipeline incorporates critical data cleaning steps--including inversion of y-coordinates for intuitive spatial interpretation, removal of zero-value frames, and exclusion of irrelevant body part labels. Additionally, it integrates robust, dual-stage filtering methods based on likelihood scores and positional changes, significantly enhancing the accuracy and consistency of data. Finally, our approach offers multiple interpolation strategies, effectively managing missing values to maintain data continuity and integrity. We evaluated the performance and versatility of our pipeline across two distinct datasets: controlled locomotion in cattle and field-recorded, highly variable trotting horse videos. Our results demonstrated substantial improvements in the quality and interpretability of processed outputs, transforming initially noisy and inconsistent data into physiologically meaningful kinematic patterns. RefineDLC successfully reduced data variability and eliminated false-positive labeling errors, offering reliable and ready-to-analyze datasets irrespective of recording conditions and animal species. By simplifying the transformation from raw DeepLabCut outputs to meaningful kinematic insights, refineDLC significantly enhances accessibility and usability, enabling a broader range of researchers--especially those with limited programming expertise--to perform precise quantitative analyses. Looking forward, adaptive filtering algorithms and real-time quality assessment features are potential enhancements that could further optimize performance, expand pipeline applicability, and automate analysis. Thus, RefineDLC not only addresses the current limitations in markerless tracking technologies but also sets the stage for future advancements in precision phenotyping, behavioral ecology, animal science, and conservation biology.