Biomedical Engineering and Telemedicine Research Group, Systems and Automation Engineering Area, Department of Automation Engineering, Electronics and Computer Architecture and Networks, Universidad de Cádiz, 11009 Cádiz, Spain.
Instituto de Investigación e Innovación Biomédica de Cádiz (INiBICA), 11009 Cádiz, Spain.
Sensors (Basel). 2021 Oct 26;21(21):7106. doi: 10.3390/s21217106.
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.
来自灵长类动物和啮齿动物研究的有力证据表明，瞳孔直径的变化可能反映蓝斑核（LC）的神经活动。瞳孔测量是唯一可用的非侵入性技术，可以作为 LC 体内活性变化的可靠且易于获取的实时生物标志物。然而，瞳孔测量在啮齿动物的临床前研究中的应用尚未完全标准化。最近的科学文献中记录了一些用于图像记录或动物和摄像机定位的组件的技术规格缺乏共识。在这项研究中，提出了一种新的瞳孔测量系统，可实时间接评估麻醉啮齿动物 LC 的功能。该系统包括基于深度学习 SOLOv2 实例的快速分割框架和一个平台，用于放置实验对象、用于数据采集的摄像机和光源。使用验证集和外部测试集评估了所提出的设置的性能，并与其他基线方法进行了比较。在后一种情况下，所选方法的交并比为 0.93，平均绝对百分比误差为 1.89%。Bland-Altman 分析表明存在极好的一致性。结果证实了高精度，使得该系统能够实时跟踪瞳孔大小，而与瞳孔大小、光强度或镇静小鼠记录过程中的任何特征无关。该框架可用于任何具有镇静或固定头部动物的神经生理学研究。
