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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...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2021
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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. |
format | Online Article Text |
id | pubmed-8581673 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
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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