Pupil-DLC: An open-source deep learning pipeline for scalable, marker-less tracking of pupil dynamics across conscious and unconscious states.
Journal of neuroscience methods July 4, 2026 Parsa Seyfourian, Lydia C Marks, Leslie D Claar et al.
Pupil diameter is a non-invasive biomarker of brain state, correlating with arousal, attention, cognitive processing, and consciousness. Existing pupillometry software often lacks scalability and robustness across diverse experimental conditions and species. Pupil-DLC is an open-source, offline, DeepLabCut-based pipeline for scalable, marker-less pupil tracking, primarily designed for mice. Trained on 21,909 manually annotated frames from over 140 videos of head-fixed mice spanning wakefulness and drug-induced states, including psychedelics and anesthesia, the dataset was deliberately selected to maximize pupil size variability and model generalization.