Automated tracking of whiskers in videos of head fixed rodents.
Identifieur interne : 000B67 ( PubMed/Corpus ); précédent : 000B66; suivant : 000B68Automated tracking of whiskers in videos of head fixed rodents.
Auteurs : Nathan G. Clack ; Daniel H. O'Connor ; Daniel Huber ; Leopoldo Petreanu ; Andrew Hires ; Simon Peron ; Karel Svoboda ; Eugene W. MyersSource :
- PLoS computational biology [ 1553-7358 ] ; 2012.
English descriptors
- KwdEn :
- MESH :
- methods : Computational Biology, Image Processing, Computer-Assisted, Videotape Recording.
- physiology : Exploratory Behavior, Vibrissae.
- Animals, Mice.
Abstract
We have developed software for fully automated tracking of vibrissae (whiskers) in high-speed videos (>500 Hz) of head-fixed, behaving rodents trimmed to a single row of whiskers. Performance was assessed against a manually curated dataset consisting of 1.32 million video frames comprising 4.5 million whisker traces. The current implementation detects whiskers with a recall of 99.998% and identifies individual whiskers with 99.997% accuracy. The average processing rate for these images was 8 Mpx/s/cpu (2.6 GHz Intel Core2, 2 GB RAM). This translates to 35 processed frames per second for a 640 px×352 px video of 4 whiskers. The speed and accuracy achieved enables quantitative behavioral studies where the analysis of millions of video frames is required. We used the software to analyze the evolving whisking strategies as mice learned a whisker-based detection task over the course of 6 days (8148 trials, 25 million frames) and measure the forces at the sensory follicle that most underlie haptic perception.
DOI: 10.1371/journal.pcbi.1002591
PubMed: 22792058
Links to Exploration step
pubmed:22792058Le document en format XML
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<author><name sortKey="Clack, Nathan G" sort="Clack, Nathan G" uniqKey="Clack N" first="Nathan G" last="Clack">Nathan G. Clack</name>
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<front><div type="abstract" xml:lang="en">We have developed software for fully automated tracking of vibrissae (whiskers) in high-speed videos (>500 Hz) of head-fixed, behaving rodents trimmed to a single row of whiskers. Performance was assessed against a manually curated dataset consisting of 1.32 million video frames comprising 4.5 million whisker traces. The current implementation detects whiskers with a recall of 99.998% and identifies individual whiskers with 99.997% accuracy. The average processing rate for these images was 8 Mpx/s/cpu (2.6 GHz Intel Core2, 2 GB RAM). This translates to 35 processed frames per second for a 640 px×352 px video of 4 whiskers. The speed and accuracy achieved enables quantitative behavioral studies where the analysis of millions of video frames is required. We used the software to analyze the evolving whisking strategies as mice learned a whisker-based detection task over the course of 6 days (8148 trials, 25 million frames) and measure the forces at the sensory follicle that most underlie haptic perception.</div>
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<Abstract><AbstractText>We have developed software for fully automated tracking of vibrissae (whiskers) in high-speed videos (>500 Hz) of head-fixed, behaving rodents trimmed to a single row of whiskers. Performance was assessed against a manually curated dataset consisting of 1.32 million video frames comprising 4.5 million whisker traces. The current implementation detects whiskers with a recall of 99.998% and identifies individual whiskers with 99.997% accuracy. The average processing rate for these images was 8 Mpx/s/cpu (2.6 GHz Intel Core2, 2 GB RAM). This translates to 35 processed frames per second for a 640 px×352 px video of 4 whiskers. The speed and accuracy achieved enables quantitative behavioral studies where the analysis of millions of video frames is required. We used the software to analyze the evolving whisking strategies as mice learned a whisker-based detection task over the course of 6 days (8148 trials, 25 million frames) and measure the forces at the sensory follicle that most underlie haptic perception.</AbstractText>
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