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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...

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Autores principales: Sakamoto, Naoaki, Kobayashi, Koji, Yamamoto, Teruko, Masuko, Sakura, Yamamoto, Masahito, Murata, Takahisa
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
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.
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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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