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

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...

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Autores principales:	Alejandro Lara-Doña, Sonia Torres-Sanchez, Blanca Priego-Torres, Esther Berrocoso, Daniel Sanchez-Morillo
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	pupillometry locus coeruleus pupil size image processing deep learning machine learning Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/e2400d018c254fb1878b6b9113677e1c
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id	oai:doaj.org-article:e2400d018c254fb1878b6b9113677e1c
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spelling	oai:doaj.org-article:e2400d018c254fb1878b6b9113677e1c2021-11-11T19:06:52ZAutomated Mouse Pupil Size Measurement System to Assess Locus Coeruleus Activity with a Deep Learning-Based Approach10.3390/s212171061424-8220https://doaj.org/article/e2400d018c254fb1878b6b9113677e1c2021-10-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/7106https://doaj.org/toc/1424-8220Strong 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.Alejandro Lara-DoñaSonia Torres-SanchezBlanca Priego-TorresEsther BerrocosoDaniel Sanchez-MorilloMDPI AGarticlepupillometrylocus coeruleuspupil sizeimage processingdeep learningmachine learningChemical technologyTP1-1185ENSensors, Vol 21, Iss 7106, p 7106 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	pupillometry locus coeruleus pupil size image processing deep learning machine learning Chemical technology TP1-1185
spellingShingle	pupillometry locus coeruleus pupil size image processing deep learning machine learning Chemical technology TP1-1185 Alejandro Lara-Doña Sonia Torres-Sanchez Blanca Priego-Torres Esther Berrocoso Daniel Sanchez-Morillo Automated Mouse Pupil Size Measurement System to Assess Locus Coeruleus Activity with a Deep Learning-Based Approach
description	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.
format	article
author	Alejandro Lara-Doña Sonia Torres-Sanchez Blanca Priego-Torres Esther Berrocoso Daniel Sanchez-Morillo
author_facet	Alejandro Lara-Doña Sonia Torres-Sanchez Blanca Priego-Torres Esther Berrocoso Daniel Sanchez-Morillo
author_sort	Alejandro Lara-Doña
title	Automated Mouse Pupil Size Measurement System to Assess Locus Coeruleus Activity with a Deep Learning-Based Approach
title_short	Automated Mouse Pupil Size Measurement System to Assess Locus Coeruleus Activity with a Deep Learning-Based Approach
title_full	Automated Mouse Pupil Size Measurement System to Assess Locus Coeruleus Activity with a Deep Learning-Based Approach
title_fullStr	Automated Mouse Pupil Size Measurement System to Assess Locus Coeruleus Activity with a Deep Learning-Based Approach
title_full_unstemmed	Automated Mouse Pupil Size Measurement System to Assess Locus Coeruleus Activity with a Deep Learning-Based Approach
title_sort	automated mouse pupil size measurement system to assess locus coeruleus activity with a deep learning-based approach
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/e2400d018c254fb1878b6b9113677e1c
work_keys_str_mv	AT alejandrolaradona automatedmousepupilsizemeasurementsystemtoassesslocuscoeruleusactivitywithadeeplearningbasedapproach AT soniatorressanchez automatedmousepupilsizemeasurementsystemtoassesslocuscoeruleusactivitywithadeeplearningbasedapproach AT blancapriegotorres automatedmousepupilsizemeasurementsystemtoassesslocuscoeruleusactivitywithadeeplearningbasedapproach AT estherberrocoso automatedmousepupilsizemeasurementsystemtoassesslocuscoeruleusactivitywithadeeplearningbasedapproach AT danielsanchezmorillo automatedmousepupilsizemeasurementsystemtoassesslocuscoeruleusactivitywithadeeplearningbasedapproach
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Automated Mouse Pupil Size Measurement System to Assess Locus Coeruleus Activity with a Deep Learning-Based Approach

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