Automated home-cage behavioral phenotyping of mice
Name
MIT-CSAIL-TR-2009-052.pdf
Size
6.58 MB
Format
Adobe PDF
Checksum (MD5)
e47d8c5f144be2a36af849641f12ce78
Author(s)
Yu, Xinlin
Steele, Andrew D.
Khilnani, Vinita
Garrote, Estibaliz
Jhuang, Hueihan
Serre, Thomas
Poggio, Tomaso
Advisor(s)
Tomaso Poggio
Date Issued
October 26, 2009
Series/Report no.
CBCL-283
MIT-CSAIL-TR-2009-052
Abstract
We describe a trainable computer vision system enabling the automated analysis of complex mouse behaviors. We provide software and a very large manually annotated video database used for training and testing the system. Our system outperforms leading commercial software and performs on par with human scoring, as measured from the ground-truth manual annotations of thousands of clips of freely behaving animals. We show that the home-cage behavior profiles provided by the system is sufficient to accurately predict the strain identity of individual animals in the case of two standard inbred and two non-standard mouse strains. Our software should complement existing sensor-based automated approaches and help develop an adaptable, comprehensive, high-throughput, fine-grained, automated analysis of rodent behavior.
Subjects
animal monitoring
rodents
Terms of Use
Creative Commons Attribution-Noncommercial 3.0 Unported
Persistent DSpace Link
