Pupil-DLC: An open-source deep learning pipeline for scalable, marker-less tracking of pupil dynamics across conscious and unconscious states.

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.

Study at a glance

Abstract

Pupil diameter is a non-invasive biomarker of brain state, correlating with arousal, attention, cognitive processing, and consciousness. However, existing pupillometry software often lacks scalability and robustness across diverse experimental conditions and species. We introduce Pupil-DLC, 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. Pupil-DLC implements a dual-model architecture: a General Model (GM) for high-throughput analysis and an Individual Model (IM) for session-specific optimization. Pupil-DLC captures pupil dynamics across awake, psychedelic, and anesthetized conditions with high agreement with ground truth and equal tracking fidelity during active locomotion and quiet rest. Confidence metrics aligned with human frame quality assessments, enabling principled tuning of accuracy-retention trade-offs. As a secondary demonstration, Pupil-DLC extends to unseen human videos across diverse conditions and frame rates, including daylight and smartphone recordings, without retraining. Pupil-DLC outperforms existing automated methods in accuracy and frame retention while maintaining computational efficiency comparable to real-time tools. These improvements stem from a learned keypoint-based representation robust to pupil shape variability, occlusions, reflections, and imaging artifacts. The GM/IM framework supports a tiered strategy balancing throughput and precision. Pupil-DLC provides a reproducible, adaptable platform for quantifying pupil-linked brain state dynamics across experimental paradigms and species, bridging basic mouse neuroscience and translational human applications.