Automated Mouse Pupil Size Measurement System to Assess Locus Coeruleus Activity with a Deep Learning-Based Approach

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2021-11Department
Ingeniería en Automática, Electrónica, Arquitectura y Redes de Computadores; PsicologíaSource
Sensors 2021, 21(21), 7106Abstract
Strong evidence from studies on primates and rodents shows that changes in pupil
diameter may reflect neural activity in the locus coeruleus (LC). Pupillometry is the only available
non-invasive technique that could be used as a reliable and easily accessible real-time biomarker of
changes in the in vivo activity of the LC. However, the application of pupillometry to preclinical
research in rodents is not yet fully standardized. A lack of consensus on the technical specifications
of some of the components used for image recording or positioning of the animal and cameras have
been recorded in recent scientific literature. In this study, a novel pupillometry system to indirectly
assess, in real-time, the function of the LC in anesthetized rodents is presented. The system comprises
a deep learning SOLOv2 instance-based fast segmentation framework and a platform designed to
place the experimental subject, the video cameras for data acquisition, and the light source. The
performance of the proposed setup was assessed and compared to other baseline methods using
a validation and an external test set. In the latter, the calculated intersection over the union was
0.93 and the mean absolute percentage error was 1.89% for the selected method. The Bland–Altman
analysis depicted an excellent agreement. The results confirmed a high accuracy that makes the
system suitable for real-time pupil size tracking, regardless of the pupil’s size, light intensity, or
any features typical of the recording process in sedated mice. The framework could be used in any
neurophysiological study with sedated or fixed-head animals.