Journal of neuroscience methods · 2026
Pupil-DLC: An open-source deep learning pipeline for scalable, marker-less tracking of pupil dynamics across conscious and unconscious states.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Seyfourian Parsa, Marks Lydia C, Claar Leslie D, Nahas Yasmeen, Keating Miles, Koch Christof, Rembado Irene
Аннотация
BACKGROUND: 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.
NEW METHOD: 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.
RESULTS: 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.
COMPARISON WITH EXISTING METHODS: 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.
CONCLUSIONS: 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.
NEW METHOD: 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.
RESULTS: 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.
COMPARISON WITH EXISTING METHODS: 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.
CONCLUSIONS: 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.