The Mouse Action Recognition System (MARS) software pipeline for automated analysis of social behaviors in mice

The study of naturalistic social behavior requires quantification of animals’ interactions. This is generally done through manual annotation—a highly time-consuming and tedious process. Recent advances in computer vision enable tracking the pose (posture) of freely behaving animals. However, automat...

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Autores principales:	Cristina Segalin, Jalani Williams, Tomomi Karigo, May Hui, Moriel Zelikowsky, Jennifer J Sun, Pietro Perona, David J Anderson, Ann Kennedy
Formato:	article
Lenguaje:	EN
Publicado:	eLife Sciences Publications Ltd 2021
Materias:	social behavior pose estimation machine learning computer vision microendoscopic imaging software Medicine R Science Q Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/e0cf9f3504db4d9c9ff63dc50df4227c
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spelling	oai:doaj.org-article:e0cf9f3504db4d9c9ff63dc50df4227c2021-11-30T15:44:26ZThe Mouse Action Recognition System (MARS) software pipeline for automated analysis of social behaviors in mice10.7554/eLife.637202050-084Xe63720https://doaj.org/article/e0cf9f3504db4d9c9ff63dc50df4227c2021-11-01T00:00:00Zhttps://elifesciences.org/articles/63720https://doaj.org/toc/2050-084XThe study of naturalistic social behavior requires quantification of animals’ interactions. This is generally done through manual annotation—a highly time-consuming and tedious process. Recent advances in computer vision enable tracking the pose (posture) of freely behaving animals. However, automatically and accurately classifying complex social behaviors remains technically challenging. We introduce the Mouse Action Recognition System (MARS), an automated pipeline for pose estimation and behavior quantification in pairs of freely interacting mice. We compare MARS’s annotations to human annotations and find that MARS’s pose estimation and behavior classification achieve human-level performance. We also release the pose and annotation datasets used to train MARS to serve as community benchmarks and resources. Finally, we introduce the Behavior Ensemble and Neural Trajectory Observatory (BENTO), a graphical user interface for analysis of multimodal neuroscience datasets. Together, MARS and BENTO provide an end-to-end pipeline for behavior data extraction and analysis in a package that is user-friendly and easily modifiable.Cristina SegalinJalani WilliamsTomomi KarigoMay HuiMoriel ZelikowskyJennifer J SunPietro PeronaDavid J AndersonAnn KennedyeLife Sciences Publications Ltdarticlesocial behaviorpose estimationmachine learningcomputer visionmicroendoscopic imagingsoftwareMedicineRScienceQBiology (General)QH301-705.5ENeLife, Vol 10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	social behavior pose estimation machine learning computer vision microendoscopic imaging software Medicine R Science Q Biology (General) QH301-705.5
spellingShingle	social behavior pose estimation machine learning computer vision microendoscopic imaging software Medicine R Science Q Biology (General) QH301-705.5 Cristina Segalin Jalani Williams Tomomi Karigo May Hui Moriel Zelikowsky Jennifer J Sun Pietro Perona David J Anderson Ann Kennedy The Mouse Action Recognition System (MARS) software pipeline for automated analysis of social behaviors in mice
description	The study of naturalistic social behavior requires quantification of animals’ interactions. This is generally done through manual annotation—a highly time-consuming and tedious process. Recent advances in computer vision enable tracking the pose (posture) of freely behaving animals. However, automatically and accurately classifying complex social behaviors remains technically challenging. We introduce the Mouse Action Recognition System (MARS), an automated pipeline for pose estimation and behavior quantification in pairs of freely interacting mice. We compare MARS’s annotations to human annotations and find that MARS’s pose estimation and behavior classification achieve human-level performance. We also release the pose and annotation datasets used to train MARS to serve as community benchmarks and resources. Finally, we introduce the Behavior Ensemble and Neural Trajectory Observatory (BENTO), a graphical user interface for analysis of multimodal neuroscience datasets. Together, MARS and BENTO provide an end-to-end pipeline for behavior data extraction and analysis in a package that is user-friendly and easily modifiable.
format	article
author	Cristina Segalin Jalani Williams Tomomi Karigo May Hui Moriel Zelikowsky Jennifer J Sun Pietro Perona David J Anderson Ann Kennedy
author_facet	Cristina Segalin Jalani Williams Tomomi Karigo May Hui Moriel Zelikowsky Jennifer J Sun Pietro Perona David J Anderson Ann Kennedy
author_sort	Cristina Segalin
title	The Mouse Action Recognition System (MARS) software pipeline for automated analysis of social behaviors in mice
title_short	The Mouse Action Recognition System (MARS) software pipeline for automated analysis of social behaviors in mice
title_full	The Mouse Action Recognition System (MARS) software pipeline for automated analysis of social behaviors in mice
title_fullStr	The Mouse Action Recognition System (MARS) software pipeline for automated analysis of social behaviors in mice
title_full_unstemmed	The Mouse Action Recognition System (MARS) software pipeline for automated analysis of social behaviors in mice
title_sort	mouse action recognition system (mars) software pipeline for automated analysis of social behaviors in mice
publisher	eLife Sciences Publications Ltd
publishDate	2021
url	https://doaj.org/article/e0cf9f3504db4d9c9ff63dc50df4227c
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The Mouse Action Recognition System (MARS) software pipeline for automated analysis of social behaviors in mice

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