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  • A YOLO based software for Automated Detection and Analysis of Rodent Behaviour in the Open Field Arena

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    Hatton-Jones493463-Published.pdf (2.653Mb)
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    Accepted Manuscript (AM)
    Author(s)
    Hatton-Jones, Kyle M
    Christie, Corey
    Griffith, Tia A
    Smith, Amanda G
    Naghipour, Saba
    Robertson, Kai
    Russell, Jake S
    Peart, Jason N
    Headrick, John P
    Cox, Amanda J
    du Toit, Eugene F
    Griffith University Author(s)
    Hatton-Jones, Kyle Mathew
    Peart, Jason N.
    Headrick, John P.
    Cox, Amanda J.
    Du Toit, Eugene
    Year published
    2021
    Metadata
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    Abstract
    Rodent models are important in mechanistic studies of the physiological and pathophysiological determinants of behaviour. The Open Field Test (OFT) is one of the most commonly utilised tests, assessing rodent behaviour in a novel open environment. The key variables assessed in an OFT are general locomotor activity and exploratory behaviours and can be assessed manually or by automated systems. Although several automated systems exist, they are often expensive, difficult to use, or limited in the type of video that can be analysed. Here we describe a machine-learning algorithm – dubbed Cosevare – that uses a trained YOLOV3 ...
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    Rodent models are important in mechanistic studies of the physiological and pathophysiological determinants of behaviour. The Open Field Test (OFT) is one of the most commonly utilised tests, assessing rodent behaviour in a novel open environment. The key variables assessed in an OFT are general locomotor activity and exploratory behaviours and can be assessed manually or by automated systems. Although several automated systems exist, they are often expensive, difficult to use, or limited in the type of video that can be analysed. Here we describe a machine-learning algorithm – dubbed Cosevare – that uses a trained YOLOV3 DNN to identify and track movement of mice in the open-field arena. We validated Cosevare’s capacity to accurately track locomotive and exploratory behaviour in 10 videos, comparing outputs generated by Cosevare with analysis by 5 manual scorers. Behavioural differences between control mice and those with diet-induced obesity (DIO) were also documented. We found the YOLOV3 based tracker to be accurate at identifying and tracking the mice within the open-field arena and in instances with variable backgrounds. Additionally, kinematic and spatial-based analysis demonstrated highly consistent scoring of locomotion, centre square duration (CSD) and entries (CSE) between Cosevare and manual scorers. Automated analysis was also able to distinguish behavioural differences between healthy control and DIO mice. The study found that a YOLOv3 based tracker is able to easily track mouse behaviour in the open field arena and supports machine learning as a potential future alternative for the assessment of animal behaviour in a wide range of species in differing environments and behavioural tests.
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    Journal Title
    Computers in Biology and Medicine
    DOI
    https://doi.org/10.1016/j.compbiomed.2021.104474
    Copyright Statement
    © 2021 Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence (http://creativecommons.org/licenses/by-nc-nd/4.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited.
    Subject
    Engineering
    Biomedical and clinical sciences
    Publication URI
    http://hdl.handle.net/10072/404612
    Collection
    • Journal articles

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