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DeepBhvTracking: A Novel Behavior Tracking Method for Laboratory Animals Based on Deep Learning

Behavioral measurement and evaluation are broadly used to understand brain functions in neuroscience, especially for investigations of movement disorders, social deficits, and mental diseases. Numerous commercial software and open-source programs have been developed for tracking the movement of labo...

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Autores principales: Sun, Guanglong, Lyu, Chenfei, Cai, Ruolan, Yu, Chencen, Sun, Hao, Schriver, Kenneth E., Gao, Lixia, Li, Xinjian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8581673/
https://www.ncbi.nlm.nih.gov/pubmed/34776893
http://dx.doi.org/10.3389/fnbeh.2021.750894
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author Sun, Guanglong
Lyu, Chenfei
Cai, Ruolan
Yu, Chencen
Sun, Hao
Schriver, Kenneth E.
Gao, Lixia
Li, Xinjian
author_facet Sun, Guanglong
Lyu, Chenfei
Cai, Ruolan
Yu, Chencen
Sun, Hao
Schriver, Kenneth E.
Gao, Lixia
Li, Xinjian
author_sort Sun, Guanglong
collection PubMed
description Behavioral measurement and evaluation are broadly used to understand brain functions in neuroscience, especially for investigations of movement disorders, social deficits, and mental diseases. Numerous commercial software and open-source programs have been developed for tracking the movement of laboratory animals, allowing animal behavior to be analyzed digitally. In vivo optical imaging and electrophysiological recording in freely behaving animals are now widely used to understand neural functions in circuits. However, it is always a challenge to accurately track the movement of an animal under certain complex conditions due to uneven environment illumination, variations in animal models, and interference from recording devices and experimenters. To overcome these challenges, we have developed a strategy to track the movement of an animal by combining a deep learning technique, the You Only Look Once (YOLO) algorithm, with a background subtraction algorithm, a method we label DeepBhvTracking. In our method, we first train the detector using manually labeled images and a pretrained deep-learning neural network combined with YOLO, then generate bounding boxes of the targets using the trained detector, and finally track the center of the targets by calculating their centroid in the bounding box using background subtraction. Using DeepBhvTracking, the movement of animals can be tracked accurately in complex environments and can be used in different behavior paradigms and for different animal models. Therefore, DeepBhvTracking can be broadly used in studies of neuroscience, medicine, and machine learning algorithms.
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spelling pubmed-85816732021-11-12 DeepBhvTracking: A Novel Behavior Tracking Method for Laboratory Animals Based on Deep Learning Sun, Guanglong Lyu, Chenfei Cai, Ruolan Yu, Chencen Sun, Hao Schriver, Kenneth E. Gao, Lixia Li, Xinjian Front Behav Neurosci Behavioral Neuroscience Behavioral measurement and evaluation are broadly used to understand brain functions in neuroscience, especially for investigations of movement disorders, social deficits, and mental diseases. Numerous commercial software and open-source programs have been developed for tracking the movement of laboratory animals, allowing animal behavior to be analyzed digitally. In vivo optical imaging and electrophysiological recording in freely behaving animals are now widely used to understand neural functions in circuits. However, it is always a challenge to accurately track the movement of an animal under certain complex conditions due to uneven environment illumination, variations in animal models, and interference from recording devices and experimenters. To overcome these challenges, we have developed a strategy to track the movement of an animal by combining a deep learning technique, the You Only Look Once (YOLO) algorithm, with a background subtraction algorithm, a method we label DeepBhvTracking. In our method, we first train the detector using manually labeled images and a pretrained deep-learning neural network combined with YOLO, then generate bounding boxes of the targets using the trained detector, and finally track the center of the targets by calculating their centroid in the bounding box using background subtraction. Using DeepBhvTracking, the movement of animals can be tracked accurately in complex environments and can be used in different behavior paradigms and for different animal models. Therefore, DeepBhvTracking can be broadly used in studies of neuroscience, medicine, and machine learning algorithms. Frontiers Media S.A. 2021-10-28 /pmc/articles/PMC8581673/ /pubmed/34776893 http://dx.doi.org/10.3389/fnbeh.2021.750894 Text en Copyright © 2021 Sun, Lyu, Cai, Yu, Sun, Schriver, Gao and Li. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Behavioral Neuroscience
Sun, Guanglong
Lyu, Chenfei
Cai, Ruolan
Yu, Chencen
Sun, Hao
Schriver, Kenneth E.
Gao, Lixia
Li, Xinjian
DeepBhvTracking: A Novel Behavior Tracking Method for Laboratory Animals Based on Deep Learning
title DeepBhvTracking: A Novel Behavior Tracking Method for Laboratory Animals Based on Deep Learning
title_full DeepBhvTracking: A Novel Behavior Tracking Method for Laboratory Animals Based on Deep Learning
title_fullStr DeepBhvTracking: A Novel Behavior Tracking Method for Laboratory Animals Based on Deep Learning
title_full_unstemmed DeepBhvTracking: A Novel Behavior Tracking Method for Laboratory Animals Based on Deep Learning
title_short DeepBhvTracking: A Novel Behavior Tracking Method for Laboratory Animals Based on Deep Learning
title_sort deepbhvtracking: a novel behavior tracking method for laboratory animals based on deep learning
topic Behavioral Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8581673/
https://www.ncbi.nlm.nih.gov/pubmed/34776893
http://dx.doi.org/10.3389/fnbeh.2021.750894
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