Scalable vision system for mouse homecage ethology

Ghadi Salem, Jonathan Krynitsky, Brett Kirkland, Eugene Lin, Aaron Chan, Simeon Anfinrud, Sarah Anderson, Marcial Garmendia-Cedillos, Rhamy Belayachi, Juan Alonso-Cruz, Joshua Yu, Anthony Iano-Fletcher, George Dold, Tom Talbot, Alexxai V. Kravitz, James B. Mitchell, Guanhang Wu, John U. Dennis, Monson Hayes, Kristin BransonThomas Pohida

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

In recent years, researchers and laboratory support companies have recognized the utility of automated profiling of laboratory mouse activity and behavior in the home-cage. Video-based systems have emerged as a viable solution for non-invasive mouse monitoring. Wider use of vision systems for ethology studies requires the development of scalable hardware seamlessly integrated with vivarium ventilated racks. Compact hardware combined with automated video analysis would greatly impact animal science and animal-based research. Automated vision systems, free of bias and intensive labor, can accurately assess rodent activity (e.g., well-being) and behavior 24-7 during research studies within primary home-cages. Scalable compact hardware designs impose constraints, such as use of fisheye lenses, placing greater burden (e.g., distorted image) on downstream video analysis algorithms. We present novel methods for analysis of video acquired through such specialized hardware. Our algorithms estimate the 3D pose of mouse from monocular images. We present a thorough examination of the algorithm training parameters’ influence on system accuracy. Overall, the methods presented offer novel approaches for accurate activity and behavior estimation practical for large-scale use of vision systems in animal facilities.

Original languageEnglish
Title of host publicationAdvanced Concepts for Intelligent Vision Systems - 17th International Conference, ACIVS 2016, Proceedings
EditorsCosimo Distante, Dan Popescu, Paul Scheunders, Wilfried Philips, Jacques Blanc-Talon
PublisherSpringer Verlag
Pages626-637
Number of pages12
ISBN (Print)9783319486796
DOIs
StatePublished - Jan 1 2016
Externally publishedYes
Event17th International Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS 2016 - Lecce, Italy
Duration: Oct 24 2016Oct 27 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10016 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference17th International Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS 2016
CountryItaly
CityLecce
Period10/24/1610/27/16

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  • Cite this

    Salem, G., Krynitsky, J., Kirkland, B., Lin, E., Chan, A., Anfinrud, S., Anderson, S., Garmendia-Cedillos, M., Belayachi, R., Alonso-Cruz, J., Yu, J., Iano-Fletcher, A., Dold, G., Talbot, T., Kravitz, A. V., Mitchell, J. B., Wu, G., Dennis, J. U., Hayes, M., ... Pohida, T. (2016). Scalable vision system for mouse homecage ethology. In C. Distante, D. Popescu, P. Scheunders, W. Philips, & J. Blanc-Talon (Eds.), Advanced Concepts for Intelligent Vision Systems - 17th International Conference, ACIVS 2016, Proceedings (pp. 626-637). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10016 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-48680-2_55