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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\u2013Altman 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\u2019s 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.<\/jats:p>","DOI":"10.3390\/s21217106","type":"journal-article","created":{"date-parts":[[2021,10,26]],"date-time":"2021-10-26T23:54:33Z","timestamp":1635292473000},"page":"7106","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Automated Mouse Pupil Size Measurement System to Assess Locus Coeruleus Activity with a Deep Learning-Based Approach"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8342-9907","authenticated-orcid":false,"given":"Alejandro","family":"Lara-Do\u00f1a","sequence":"first","affiliation":[{"name":"Biomedical Engineering and Telemedicine Research Group, Systems and Automation Engineering Area, Department of Automation Engineering, Electronics and Computer Architecture and Networks, Universidad de C\u00e1diz, 11009 C\u00e1diz, Spain"},{"name":"Instituto de Investigaci\u00f3n e Innovaci\u00f3n Biom\u00e9dica de C\u00e1diz (INiBICA), 11009 C\u00e1diz, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0255-6558","authenticated-orcid":false,"given":"Sonia","family":"Torres-Sanchez","sequence":"additional","affiliation":[{"name":"Instituto de Investigaci\u00f3n e Innovaci\u00f3n Biom\u00e9dica de C\u00e1diz (INiBICA), 11009 C\u00e1diz, Spain"},{"name":"Neuropsychopharmacology & Psychobiology Research Group, Psychobiology Area, Department of Psychology, Universidad de C\u00e1diz, 11003 C\u00e1diz, Spain"},{"name":"Centro de Investigaci\u00f3n Biom\u00e9dica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, 28029 Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1609-0429","authenticated-orcid":false,"given":"Blanca","family":"Priego-Torres","sequence":"additional","affiliation":[{"name":"Biomedical Engineering and Telemedicine Research Group, Systems and Automation Engineering Area, Department of Automation Engineering, Electronics and Computer Architecture and Networks, Universidad de C\u00e1diz, 11009 C\u00e1diz, Spain"},{"name":"Instituto de Investigaci\u00f3n e Innovaci\u00f3n Biom\u00e9dica de C\u00e1diz (INiBICA), 11009 C\u00e1diz, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0208-7472","authenticated-orcid":false,"given":"Esther","family":"Berrocoso","sequence":"additional","affiliation":[{"name":"Instituto de Investigaci\u00f3n e Innovaci\u00f3n Biom\u00e9dica de C\u00e1diz (INiBICA), 11009 C\u00e1diz, Spain"},{"name":"Neuropsychopharmacology & Psychobiology Research Group, Psychobiology Area, Department of Psychology, Universidad de C\u00e1diz, 11003 C\u00e1diz, Spain"},{"name":"Centro de Investigaci\u00f3n Biom\u00e9dica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, 28029 Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5603-0936","authenticated-orcid":false,"given":"Daniel","family":"Sanchez-Morillo","sequence":"additional","affiliation":[{"name":"Biomedical Engineering and Telemedicine Research Group, Systems and Automation Engineering Area, Department of Automation Engineering, Electronics and Computer Architecture and Networks, Universidad de C\u00e1diz, 11009 C\u00e1diz, Spain"},{"name":"Instituto de Investigaci\u00f3n e Innovaci\u00f3n Biom\u00e9dica de C\u00e1diz (INiBICA), 11009 C\u00e1diz, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"de Gee, J.W., Tsetsos, K., Schwabe, L., Urai, A.E., McCormick, D., McGinley, M.J., and Donner, T.H. (2020). Pupil-linked phasic arousal predicts a reduction of choice bias across species and decision domains. eLife, 9.","DOI":"10.7554\/eLife.54014"},{"key":"ref_2","first-page":"E618","article-title":"Decision-related pupil dilation reflects upcoming choice and individual bias","volume":"111","author":"Knapen","year":"2014","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"100","DOI":"10.3389\/fnins.2014.00100","article-title":"Pupil size and social vigilance in rhesus macaques","volume":"8","author":"Ebitz","year":"2014","journal-title":"Front. Neurosci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1016\/j.neuron.2014.09.033","article-title":"Pupil Fluctuations Track Fast Switching of Cortical States during Quiet Wakefulness","volume":"84","author":"Reimer","year":"2014","journal-title":"Neuron"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1040","DOI":"10.1038\/nn.3130","article-title":"Rational regulation of learning dynamics by pupil-linked arousal systems","volume":"15","author":"Nassar","year":"2012","journal-title":"Nat. Neurosci."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Reimer, J., McGinley, M.J., Liu, Y., Rodenkirch, C., Wang, Q., McCormick, D.A., and Tolias, A.S. (2016). Pupil fluctuations track rapid changes in adrenergic and cholinergic activity in cortex. Nat. Commun., 7.","DOI":"10.1038\/ncomms13289"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1016\/j.neuron.2015.11.028","article-title":"Relationships between Pupil Diameter and Neuronal Activity in the Locus Coeruleus, Colliculi, and Cingulate Cortex","volume":"89","author":"Joshi","year":"2016","journal-title":"Neuron"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.neuron.2015.12.031","article-title":"More than Meets the Eye: The Relationship between Pupil Size and Locus Coeruleus Activity","volume":"89","author":"Costa","year":"2016","journal-title":"Neuron"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"4140","DOI":"10.1002\/hbm.22466","article-title":"Pupil diameter covaries with BOLD activity in human locus coeruleus","volume":"35","author":"Murphy","year":"2014","journal-title":"Hum. Brain Mapp."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"8239","DOI":"10.1523\/JNEUROSCI.1164-19.2019","article-title":"Redefining Noradrenergic Neuromodulation of Behavior: Impacts of a Modular Locus Coeruleus Architecture","volume":"39","author":"Chandler","year":"2019","journal-title":"J. Neurosci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1016\/j.neuron.2012.09.011","article-title":"Orienting and Reorienting: The Locus Coeruleus Mediates Cognition through Arousal","volume":"76","author":"Sara","year":"2012","journal-title":"Neuron"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.neuroscience.2016.05.057","article-title":"Noradrenergic Locus Coeruleus pathways in pain modulation","volume":"338","author":"Borges","year":"2016","journal-title":"Neuroscience"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1007\/s00441-017-2649-1","article-title":"Locus coeruleus","volume":"373","author":"Benarroch","year":"2018","journal-title":"Cell Tissue Res."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1016\/j.tins.2018.01.010","article-title":"Long Road to Ruin: Noradrenergic Dysfunction in Neurodegenerative Disease","volume":"41","author":"Weinshenker","year":"2018","journal-title":"Trends Neurosci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2558","DOI":"10.1093\/brain\/awz193","article-title":"Locus coeruleus imaging as a biomarker for noradrenergic dysfunction in neurodegenerative diseases","volume":"142","author":"Betts","year":"2019","journal-title":"Brain"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2014","DOI":"10.1016\/j.pain.2013.06.021","article-title":"Social stress exacerbates the aversion to painful experiences in rats exposed to chronic pain: The role of the locus coeruleus","volume":"154","author":"Bravo","year":"2013","journal-title":"Pain"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.biopsych.2012.06.033","article-title":"Chronic pain leads to concomitant noradrenergic impairment and mood disorders","volume":"73","author":"Horrillo","year":"2013","journal-title":"Biol. Psychiatry"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1021","DOI":"10.1016\/j.biopsych.2019.02.018","article-title":"Chemogenetic Silencing of the Locus Coeruleus\u2013Basolateral Amygdala Pathway Abolishes Pain-Induced Anxiety and Enhanced Aversive Learning in Rats","volume":"85","author":"Bravo","year":"2019","journal-title":"Biol. Psychiatry"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"996","DOI":"10.1016\/j.euroneuro.2014.01.011","article-title":"Pain exacerbates chronic mild stress-induced changes in noradrenergic transmission in rats","volume":"24","author":"Bravo","year":"2014","journal-title":"Eur. Neuropsychopharmacol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1007\/s11910-020-01087-7","article-title":"Locus Coeruleus Magnetic Resonance Imaging in Neurological Diseases","volume":"21","author":"Galgani","year":"2021","journal-title":"Curr. Neurol. Neurosci. Rep."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2301","DOI":"10.1038\/s41596-020-0324-6","article-title":"A complete pupillometry toolbox for real-time monitoring of locus coeruleus activity in rodents","volume":"15","author":"Privitera","year":"2020","journal-title":"Nat. Protoc."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1361","DOI":"10.1038\/s41598-018-37227-w","article-title":"Behavioral correlates of activity of optogenetically identified locus coeruleus noradrenergic neurons in rats performing T-maze tasks","volume":"9","author":"Xiang","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"702","DOI":"10.1016\/j.neuron.2019.05.034","article-title":"Rapid Reconfiguration of the Functional Connectome after Chemogenetic Locus Coeruleus Activation","volume":"103","author":"Zerbi","year":"2019","journal-title":"Neuron"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"3099","DOI":"10.1016\/j.celrep.2017.08.094","article-title":"Dynamic Lateralization of Pupil Dilation Evoked by Locus Coeruleus Activation Results from Sympathetic, Not Parasympathetic, Contributions","volume":"20","author":"Liu","year":"2017","journal-title":"Cell Rep."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1069","DOI":"10.3389\/fnins.2020.583421","article-title":"What\u2019s That (Blue) Spot on my MRI? Multimodal Neuroimaging of the Locus Coeruleus in Neurodegenerative Disease","volume":"14","author":"Kelberman","year":"2020","journal-title":"Front. Neurosci."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2228","DOI":"10.1073\/pnas.1712268115","article-title":"Locus coeruleus integrity in old age is selectively related to memories linked with salient negative events","volume":"115","author":"Callaghan","year":"2018","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.cortex.2017.09.025","article-title":"Task-evoked pupil dilation and BOLD variance as indicators of locus coeruleus dysfunction","volume":"97","author":"Elman","year":"2017","journal-title":"Cortex"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1016\/j.biopsych.2017.08.021","article-title":"Locus Coeruleus Activity Mediates Hyperresponsiveness in Posttraumatic Stress Disorder","volume":"83","author":"Naegeli","year":"2018","journal-title":"Biol. Psychiatry"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1038\/s42255-020-0170-4","article-title":"Arousal-induced cortical activity triggers lactate release from astrocytes","volume":"2","author":"Zuend","year":"2020","journal-title":"Nat. Metab."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1038\/s41593-018-0305-z","article-title":"Active control of arousal by a locus coeruleus GABAergic circuit","volume":"22","author":"Sur","year":"2019","journal-title":"Nat. Neurosci."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Hayat, H., Regev, N., Matosevich, N., Sales, A., Paredes-Rodriguez, E., Krom, A.J., Bergman, L., Li, Y., Lavigne, M., and Kremer, E.J. (2020). Locus coeruleus norepinephrine activity mediates sensory-evoked awakenings from sleep. Sci. Adv., 6.","DOI":"10.1126\/sciadv.aaz4232"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"392","DOI":"10.1016\/j.cub.2017.12.049","article-title":"Pupil Size Coupling to Cortical States Protects the Stability of Deep Sleep via Parasympathetic Modulation","volume":"28","author":"Prsa","year":"2018","journal-title":"Curr. Biol."},{"key":"ref_33","unstructured":"Andreassi, J.L. (2006). Pupillary Response and Behavior. Psychophysiology: Human Behavior and Physiological Response, Psychology Press."},{"key":"ref_34","first-page":"2152","article-title":"Using DeepLabCut for 3D markerless pose estimation across species and behaviors","volume":"14","author":"Nath","year":"2018","journal-title":"bioRxiv"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Ou, W.L., Kuo, T.L., Chang, C.C., and Fan, C.P. (2021). Deep-Learning-Based Pupil Center Detection and Tracking Technology for Visible-Light Wearable Gaze Tracking Devices. Appl. Sci., 11.","DOI":"10.3390\/app11020851"},{"key":"ref_36","unstructured":"Fuhl, W., Santini, T., Kasneci, G., Rosenstiel, W., and Kasneci, E. (2017). PupilNet v2.0: Convolutional Neural Networks for CPU based real time Robust Pupil Detection. arXiv."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"85","DOI":"10.3233\/ICA-180584","article-title":"DeepEye: Deep convolutional network for pupil detection in real environments","volume":"26","author":"Pardo","year":"2018","journal-title":"Integr. Comput. Aided Eng."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"108307","DOI":"10.1016\/j.jneumeth.2019.05.016","article-title":"DeepVOG: Open-source pupil segmentation and gaze estimation in neuroscience using deep learning","volume":"324","author":"Yiu","year":"2019","journal-title":"J. Neurosci. Methods"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Lee, K.I., Jeon, J.H., and Song, B.C. (2020). Deep Learning-Based Pupil Center Detection for Fast and Accurate Eye Tracking System, Springer. Lecture Notes in Computer Science.","DOI":"10.1007\/978-3-030-58529-7_3"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.autneu.2018.05.007","article-title":"Impaired pupillary control in \u201cschizophrenia-like\u201d WISKET rats","volume":"213","author":"Kekesi","year":"2018","journal-title":"Auton. Neurosci."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1093\/ijnp\/pyx005","article-title":"Activation of Extracellular Signal-Regulated Kinases (ERK 1\/2) in the Locus Coeruleus Contributes to Pain-Related Anxiety in Arthritic Male Rats","volume":"20","author":"Borges","year":"2017","journal-title":"Int. J. Neuropsychopharmacol."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"36","DOI":"10.3389\/frobt.2015.00036","article-title":"A taxonomy of deep convolutional neural nets for computer vision","volume":"2","author":"Srinivas","year":"2016","journal-title":"Front. Robot. AI"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Sultana, F., Sufian, A., and Dutta, P. (2020). Evolution of Image Segmentation using Deep Convolutional Neural Network: A Survey. Knowl. Based Syst., 201\u2013202.","DOI":"10.1016\/j.knosys.2020.106062"},{"key":"ref_44","unstructured":"Wu, Z., Shen, C., and Hengel, A.v.d. (2016). Bridging Category-level and Instance-level Semantic Image Segmentation. arXiv."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1109\/TPAMI.2018.2844175","article-title":"Mask R-CNN","volume":"42","author":"He","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Zhang, R., Tian, Z., Shen, C., You, M., and Yan, Y. (2020, January 16\u201318). Mask Encoding for Single Shot Instance Segmentation. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01024"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Wang, Y., Xu, Z., Shen, H., Cheng, B., and Yang, L. (2020, January 16\u201318). CenterMask: Single shot instance segmentation with point representation. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00933"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Bolya, D., Zhou, C., Xiao, F., and Lee, Y.J. (2019\u20132, January 27). YOLACT: Real-time instance segmentation. Proceedings of the IEEE International Conference on Computer Vision, Sepul, Korea.","DOI":"10.1109\/ICCV.2019.00925"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Chen, X., Girshick, R., He, K., and Dollar, P. (2019\u20132, January 27). TensorMask: A foundation for dense object segmentation. Proceedings of the IEEE International Conference on Computer Vision, Sepul, Korea.","DOI":"10.1109\/ICCV.2019.00215"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Zagoruyko, S., Lerer, A., Lin, T.Y., Pinheiro, P.O., Gross, S., Chintala, S., and Doll r, P. (2016, January 19\u201322). A multipath network for object detection. Proceedings of the British Machine Vision Conference 2016, BMVC 2016, York, UK.","DOI":"10.5244\/C.30.15"},{"key":"ref_51","unstructured":"Pinheiro, P.O., Collobert, R., and Dollar, P. (2015, January 7\u201312). Learning to Segment Object Candidates. Proceedings of the 28th International Conference on Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_52","unstructured":"Wang, X., Zhang, R., Kong, T., Li, L., and Shen, C. (2020). SOLOv2: Dynamic and Fast Instance Segmentation. arXiv."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Dollar, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21\u201326). Feature Pyramid Networks for Object Detection. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"197","DOI":"10.14232\/actacyb.24.2.2019.3","article-title":"Automating, analyzing and improving pupillometry with machine learning algorithms","volume":"24","year":"2019","journal-title":"Acta Cybern."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"833","DOI":"10.1007\/978-3-030-01234-2_49","article-title":"Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation","volume":"Volume 11211","author":"Chen","year":"2018","journal-title":"Proceedings of the Computer Vision\u2014ECCV 2018"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Chollet, F. (2017, January 21\u201326). Xception: Deep Learning With Depthwise Separable Convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2017, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.C. (2018, January 18\u201323). MobileNetV2: Inverted Residuals and Linear Bottlenecks. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"640","DOI":"10.1109\/TPAMI.2016.2572683","article-title":"Fully Convolutional Networks for Semantic Segmentation","volume":"39","author":"Shelhamer","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Kim, T., and Lee, E.C. (2020). Experimental verification of objective visual fatigue measurement based on accurate pupil detection of infrared eye image and multi-feature analysis. Sensors, 20.","DOI":"10.3390\/s20174814"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1109\/TPAMI.2018.2858826","article-title":"Focal Loss for Dense Object Detection","volume":"42","author":"Lin","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"649","DOI":"10.1007\/978-3-030-58523-5_38","article-title":"SOLO: Segmenting Objects by Locations","volume":"Volume 12363","author":"Wang","year":"2020","journal-title":"Lecture Notes in Computer Science"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1016\/S0140-6736(86)90837-8","article-title":"Statistical Methods for Assessing Agreement Between Two Methods of Clinical Measurement","volume":"327","author":"Altman","year":"1986","journal-title":"Lancet"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/21\/7106\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:24:13Z","timestamp":1760167453000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/21\/7106"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,26]]},"references-count":64,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2021,11]]}},"alternative-id":["s21217106"],"URL":"https:\/\/doi.org\/10.3390\/s21217106","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10,26]]}}}