refineDLC: An advanced post-processing pipeline for DeepLabCut outputs.

Area of Science:

• Animal Behavior
• Biomechanical Analysis
• Computational Biology

Background:

• DeepLabCut enables markerless pose estimation for behavioral research.
• Quantitative kinematic analysis is often limited by noisy DeepLabCut outputs and requires significant computational expertise.

Purpose of the Study:

• To introduce refineDLC, a post-processing pipeline to convert noisy DeepLabCut outputs into robust kinematic data.
• To enhance the accessibility of quantitative kinematic analysis for researchers with limited programming expertise.

Main Methods:

• The refineDLC pipeline includes y-coordinate inversion, removal of zero-value frames, and exclusion of irrelevant body part labels.
• Dual-stage filtering based on likelihood scores and positional changes is applied.
• Multiple interpolation strategies are used to manage missing values.

Main Results:

• refineDLC substantially improved data quality and interpretability in cattle locomotion and horse trotting datasets.
• The pipeline reduced variability, eliminated false-positive labeling errors, and transformed noisy trajectories into meaningful kinematic patterns.
• Analysis-ready outputs were achieved regardless of recording conditions or species.

Conclusions:

• refineDLC simplifies the transformation of raw pose estimation data into reliable kinematic insights.
• The pipeline enhances the accessibility of precise quantitative analyses for a broader range of researchers.
• Future developments aim to optimize performance and automation for applications in precision phenotyping and conservation biology.