An assistive computer vision tool to automatically detect changes in fish behavior in response to ambient odor

Abstract The analysis of fish behavior in response to odor stimulation is a crucial component of the general study of cross-modal sensory integration in vertebrates. In zebrafish, the centrifugal pathway runs between the olfactory bulb and the neural retina, originating at the terminalis neuron in t...

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Autores principales:	Sreya Banerjee, Lauren Alvey, Paula Brown, Sophie Yue, Lei Li, Walter J. Scheirer
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/244abfd689fa4362aebed11edb8247a6
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spelling	oai:doaj.org-article:244abfd689fa4362aebed11edb8247a62021-12-02T14:01:37ZAn assistive computer vision tool to automatically detect changes in fish behavior in response to ambient odor10.1038/s41598-020-79772-32045-2322https://doaj.org/article/244abfd689fa4362aebed11edb8247a62021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-79772-3https://doaj.org/toc/2045-2322Abstract The analysis of fish behavior in response to odor stimulation is a crucial component of the general study of cross-modal sensory integration in vertebrates. In zebrafish, the centrifugal pathway runs between the olfactory bulb and the neural retina, originating at the terminalis neuron in the olfactory bulb. Any changes in the ambient odor of a fish’s environment warrant a change in visual sensitivity and can trigger mating-like behavior in males due to increased GnRH signaling in the terminalis neuron. Behavioral experiments to study this phenomenon are commonly conducted in a controlled environment where a video of the fish is recorded over time before and after the application of chemicals to the water. Given the subtleties of behavioral change, trained biologists are currently required to annotate such videos as part of a study. This process of manually analyzing the videos is time-consuming, requires multiple experts to avoid human error/bias and cannot be easily crowdsourced on the Internet. Machine learning algorithms from computer vision, on the other hand, have proven to be effective for video annotation tasks because they are fast, accurate, and, if designed properly, can be less biased than humans. In this work, we propose to automate the entire process of analyzing videos of behavior changes in zebrafish by using tools from computer vision, relying on minimal expert supervision. The overall objective of this work is to create a generalized tool to predict animal behaviors from videos using state-of-the-art deep learning models, with the dual goal of advancing understanding in biology and engineering a more robust and powerful artificial information processing system for biologists.Sreya BanerjeeLauren AlveyPaula BrownSophie YueLei LiWalter J. ScheirerNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Sreya Banerjee Lauren Alvey Paula Brown Sophie Yue Lei Li Walter J. Scheirer An assistive computer vision tool to automatically detect changes in fish behavior in response to ambient odor
description	Abstract The analysis of fish behavior in response to odor stimulation is a crucial component of the general study of cross-modal sensory integration in vertebrates. In zebrafish, the centrifugal pathway runs between the olfactory bulb and the neural retina, originating at the terminalis neuron in the olfactory bulb. Any changes in the ambient odor of a fish’s environment warrant a change in visual sensitivity and can trigger mating-like behavior in males due to increased GnRH signaling in the terminalis neuron. Behavioral experiments to study this phenomenon are commonly conducted in a controlled environment where a video of the fish is recorded over time before and after the application of chemicals to the water. Given the subtleties of behavioral change, trained biologists are currently required to annotate such videos as part of a study. This process of manually analyzing the videos is time-consuming, requires multiple experts to avoid human error/bias and cannot be easily crowdsourced on the Internet. Machine learning algorithms from computer vision, on the other hand, have proven to be effective for video annotation tasks because they are fast, accurate, and, if designed properly, can be less biased than humans. In this work, we propose to automate the entire process of analyzing videos of behavior changes in zebrafish by using tools from computer vision, relying on minimal expert supervision. The overall objective of this work is to create a generalized tool to predict animal behaviors from videos using state-of-the-art deep learning models, with the dual goal of advancing understanding in biology and engineering a more robust and powerful artificial information processing system for biologists.
format	article
author	Sreya Banerjee Lauren Alvey Paula Brown Sophie Yue Lei Li Walter J. Scheirer
author_facet	Sreya Banerjee Lauren Alvey Paula Brown Sophie Yue Lei Li Walter J. Scheirer
author_sort	Sreya Banerjee
title	An assistive computer vision tool to automatically detect changes in fish behavior in response to ambient odor
title_short	An assistive computer vision tool to automatically detect changes in fish behavior in response to ambient odor
title_full	An assistive computer vision tool to automatically detect changes in fish behavior in response to ambient odor
title_fullStr	An assistive computer vision tool to automatically detect changes in fish behavior in response to ambient odor
title_full_unstemmed	An assistive computer vision tool to automatically detect changes in fish behavior in response to ambient odor
title_sort	assistive computer vision tool to automatically detect changes in fish behavior in response to ambient odor
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/244abfd689fa4362aebed11edb8247a6
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An assistive computer vision tool to automatically detect changes in fish behavior in response to ambient odor

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