Pupil-DLC: an open-source deep learning pipeline for scalable, markerless tracking of pupil dynamics across conscious and unconscious states

Pupil diameter provides a powerful, non-invasive biomarker of brain state, correlating with arousal, attention, cognitive processing, and level of consciousness. Despite its widespread use, pupillometry remains limited by software tools that lack scalability, robustness, and flexibility across experimental conditions. Here we introduce Pupil-DLC, an open-source, DeepLabCut-based pipeline for scalable, markerless tracking of pupil dynamics. Pupil-DLC is trained on 20,550 manually annotated frames selected from over 130 pupil videos of head-fixed and behaving mice spanning wakefulness to diverse drug-induced states of consciousness, including psychedelics, and anesthesia. The pipeline robustly captures state-dependent pupil dynamics, achieving high agreement with human-annotated ground truth data and outperforming an established automated method in both accuracy and detection reliability, while maintaining computational efficiency. Pupil-DLC incorporates a dual-model framework comprising a General Model (GM) for high-throughput analysis of infra-red recorded mouse pupils and an Individual Model (IM) tailored for session-specific optimization, as well as interpretable confidence metrics that enable principled trade-offs between accuracy and data retention. Notably, the model generalizes without retraining to human infrared pupil recordings and, with retraining, extends to human videos acquired under diverse daylight and camera conditions, enabling cross-species pupillometry. Pupil-DLC provides a flexible, reproducible platform for quantifying pupil-linked brain state dynamics across experimental paradigms and species.