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Automated Grooming Detection of Mouse by Three-Dimensional Convolutional Neural Network
Grooming is a common behavior for animals to care for their fur, maintain hygiene, and regulate body temperature. Since various factors, including stressors and genetic mutations, affect grooming quantitatively and qualitatively, the assessment of grooming is important to understand the status of ex...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8847608/ https://www.ncbi.nlm.nih.gov/pubmed/35185488 http://dx.doi.org/10.3389/fnbeh.2022.797860 |
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author | Sakamoto, Naoaki Kobayashi, Koji Yamamoto, Teruko Masuko, Sakura Yamamoto, Masahito Murata, Takahisa |
author_facet | Sakamoto, Naoaki Kobayashi, Koji Yamamoto, Teruko Masuko, Sakura Yamamoto, Masahito Murata, Takahisa |
author_sort | Sakamoto, Naoaki |
collection | PubMed |
description | Grooming is a common behavior for animals to care for their fur, maintain hygiene, and regulate body temperature. Since various factors, including stressors and genetic mutations, affect grooming quantitatively and qualitatively, the assessment of grooming is important to understand the status of experimental animals. However, current grooming detection methods are time-consuming, laborious, and require specialized equipment. In addition, they generally cannot discriminate grooming microstructures such as face washing and body licking. In this study, we aimed to develop an automated grooming detection method that can distinguish facial grooming from body grooming by image analysis using artificial intelligence. Mouse behavior was recorded using a standard hand camera. We carefully observed videos and labeled each time point as facial grooming, body grooming, and not grooming. We constructed a three-dimensional convolutional neural network (3D-CNN) and trained it using the labeled images. Since the output of the trained 3D-CNN included unlikely short grooming bouts and interruptions, we set posterior filters to remove them. The performance of the trained 3D-CNN and filters was evaluated using a first-look dataset that was not used for training. The sensitivity of facial and body grooming detection reached 81.3% and 91.9%, respectively. The positive predictive rates of facial and body grooming detection were 83.5% and 88.5%, respectively. The number of grooming bouts predicted by our method was highly correlated with human observations (face: r = 0.93, body: r = 0.98). These results highlight that our method has sufficient ability to distinguish facial grooming and body grooming in mice. |
format | Online Article Text |
id | pubmed-8847608 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-88476082022-02-17 Automated Grooming Detection of Mouse by Three-Dimensional Convolutional Neural Network Sakamoto, Naoaki Kobayashi, Koji Yamamoto, Teruko Masuko, Sakura Yamamoto, Masahito Murata, Takahisa Front Behav Neurosci Neuroscience Grooming is a common behavior for animals to care for their fur, maintain hygiene, and regulate body temperature. Since various factors, including stressors and genetic mutations, affect grooming quantitatively and qualitatively, the assessment of grooming is important to understand the status of experimental animals. However, current grooming detection methods are time-consuming, laborious, and require specialized equipment. In addition, they generally cannot discriminate grooming microstructures such as face washing and body licking. In this study, we aimed to develop an automated grooming detection method that can distinguish facial grooming from body grooming by image analysis using artificial intelligence. Mouse behavior was recorded using a standard hand camera. We carefully observed videos and labeled each time point as facial grooming, body grooming, and not grooming. We constructed a three-dimensional convolutional neural network (3D-CNN) and trained it using the labeled images. Since the output of the trained 3D-CNN included unlikely short grooming bouts and interruptions, we set posterior filters to remove them. The performance of the trained 3D-CNN and filters was evaluated using a first-look dataset that was not used for training. The sensitivity of facial and body grooming detection reached 81.3% and 91.9%, respectively. The positive predictive rates of facial and body grooming detection were 83.5% and 88.5%, respectively. The number of grooming bouts predicted by our method was highly correlated with human observations (face: r = 0.93, body: r = 0.98). These results highlight that our method has sufficient ability to distinguish facial grooming and body grooming in mice. Frontiers Media S.A. 2022-02-02 /pmc/articles/PMC8847608/ /pubmed/35185488 http://dx.doi.org/10.3389/fnbeh.2022.797860 Text en Copyright © 2022 Sakamoto, Kobayashi, Yamamoto, Masuko, Yamamoto and Murata. 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 | Neuroscience Sakamoto, Naoaki Kobayashi, Koji Yamamoto, Teruko Masuko, Sakura Yamamoto, Masahito Murata, Takahisa Automated Grooming Detection of Mouse by Three-Dimensional Convolutional Neural Network |
title | Automated Grooming Detection of Mouse by Three-Dimensional Convolutional Neural Network |
title_full | Automated Grooming Detection of Mouse by Three-Dimensional Convolutional Neural Network |
title_fullStr | Automated Grooming Detection of Mouse by Three-Dimensional Convolutional Neural Network |
title_full_unstemmed | Automated Grooming Detection of Mouse by Three-Dimensional Convolutional Neural Network |
title_short | Automated Grooming Detection of Mouse by Three-Dimensional Convolutional Neural Network |
title_sort | automated grooming detection of mouse by three-dimensional convolutional neural network |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8847608/ https://www.ncbi.nlm.nih.gov/pubmed/35185488 http://dx.doi.org/10.3389/fnbeh.2022.797860 |
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