Automated detection of mouse scratching behaviour using convolutional recurrent neural network
Abstract Scratching is one of the most important behaviours in experimental animals because it can reflect itching and/or psychological stress. Here, we aimed to establish a novel method to detect scratching using deep neural network. Scratching was elicited by injecting a chemical pruritogen lysoph...
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Nature Portfolio
2021
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oai:doaj.org-article:720635a5c04e4738bbc445a00fa4c2ea2021-12-02T14:12:48ZAutomated detection of mouse scratching behaviour using convolutional recurrent neural network10.1038/s41598-020-79965-w2045-2322https://doaj.org/article/720635a5c04e4738bbc445a00fa4c2ea2021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-79965-whttps://doaj.org/toc/2045-2322Abstract Scratching is one of the most important behaviours in experimental animals because it can reflect itching and/or psychological stress. Here, we aimed to establish a novel method to detect scratching using deep neural network. Scratching was elicited by injecting a chemical pruritogen lysophosphatidic acid to the back of a mouse, and behaviour was recorded using a standard handy camera. Images showing differences between two consecutive frames in each video were generated, and each frame was manually labelled as showing scratching behaviour or not. Next, a convolutional recurrent neural network (CRNN), composed of sequential convolution, recurrent, and fully connected blocks, was constructed. The CRNN was trained using the manually labelled images and then evaluated for accuracy using a first-look dataset. Sensitivity and positive predictive rates reached 81.6% and 87.9%, respectively. The predicted number and durations of scratching events correlated with those of the human observation. The trained CRNN could also successfully detect scratching in the hapten-induced atopic dermatitis mouse model (sensitivity, 94.8%; positive predictive rate, 82.1%). In conclusion, we established a novel scratching detection method using CRNN and showed that it can be used to study disease models.Koji KobayashiSeiji MatsushitaNaoyuki ShimizuSakura MasukoMasahito YamamotoTakahisa MurataNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021) |
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Medicine R Science Q |
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Medicine R Science Q Koji Kobayashi Seiji Matsushita Naoyuki Shimizu Sakura Masuko Masahito Yamamoto Takahisa Murata Automated detection of mouse scratching behaviour using convolutional recurrent neural network |
| description |
Abstract Scratching is one of the most important behaviours in experimental animals because it can reflect itching and/or psychological stress. Here, we aimed to establish a novel method to detect scratching using deep neural network. Scratching was elicited by injecting a chemical pruritogen lysophosphatidic acid to the back of a mouse, and behaviour was recorded using a standard handy camera. Images showing differences between two consecutive frames in each video were generated, and each frame was manually labelled as showing scratching behaviour or not. Next, a convolutional recurrent neural network (CRNN), composed of sequential convolution, recurrent, and fully connected blocks, was constructed. The CRNN was trained using the manually labelled images and then evaluated for accuracy using a first-look dataset. Sensitivity and positive predictive rates reached 81.6% and 87.9%, respectively. The predicted number and durations of scratching events correlated with those of the human observation. The trained CRNN could also successfully detect scratching in the hapten-induced atopic dermatitis mouse model (sensitivity, 94.8%; positive predictive rate, 82.1%). In conclusion, we established a novel scratching detection method using CRNN and showed that it can be used to study disease models. |
| format |
article |
| author |
Koji Kobayashi Seiji Matsushita Naoyuki Shimizu Sakura Masuko Masahito Yamamoto Takahisa Murata |
| author_facet |
Koji Kobayashi Seiji Matsushita Naoyuki Shimizu Sakura Masuko Masahito Yamamoto Takahisa Murata |
| author_sort |
Koji Kobayashi |
| title |
Automated detection of mouse scratching behaviour using convolutional recurrent neural network |
| title_short |
Automated detection of mouse scratching behaviour using convolutional recurrent neural network |
| title_full |
Automated detection of mouse scratching behaviour using convolutional recurrent neural network |
| title_fullStr |
Automated detection of mouse scratching behaviour using convolutional recurrent neural network |
| title_full_unstemmed |
Automated detection of mouse scratching behaviour using convolutional recurrent neural network |
| title_sort |
automated detection of mouse scratching behaviour using convolutional recurrent neural network |
| publisher |
Nature Portfolio |
| publishDate |
2021 |
| url |
https://doaj.org/article/720635a5c04e4738bbc445a00fa4c2ea |
| work_keys_str_mv |
AT kojikobayashi automateddetectionofmousescratchingbehaviourusingconvolutionalrecurrentneuralnetwork AT seijimatsushita automateddetectionofmousescratchingbehaviourusingconvolutionalrecurrentneuralnetwork AT naoyukishimizu automateddetectionofmousescratchingbehaviourusingconvolutionalrecurrentneuralnetwork AT sakuramasuko automateddetectionofmousescratchingbehaviourusingconvolutionalrecurrentneuralnetwork AT masahitoyamamoto automateddetectionofmousescratchingbehaviourusingconvolutionalrecurrentneuralnetwork AT takahisamurata automateddetectionofmousescratchingbehaviourusingconvolutionalrecurrentneuralnetwork |
| _version_ |
1718391734103179264 |