TY - JOUR
T1 - WhiskEras
T2 - A New Algorithm for Accurate Whisker Tracking
AU - Betting, Jan-Harm L.F.
AU - Romano, Vincenzo
AU - Al-Ars, Zaid
AU - Bosman, Laurens W.J.
AU - Strydis, Christos
AU - De Zeeuw, Chris I.
PY - 2020
Y1 - 2020
N2 - Rodents engage in active touch using their facial whiskers: they explore their environment by making rapid back-and-forth movements. The fast nature of whisker movements, during which whiskers often cross each other, makes it notoriously difficult to track individual whiskers of the intact whisker field. We present here a novel algorithm, WhiskEras, for tracking of whisker movements in high-speed videos of untrimmed mice, without requiring labeled data. WhiskEras consists of a pipeline of image-processing steps: first, the points that form the whisker centerlines are detected with sub-pixel accuracy. Then, these points are clustered in order to distinguish individual whiskers. Subsequently, the whiskers are parameterized so that a single whisker can be described by four parameters. The last step consists of tracking individual whiskers over time. We describe that WhiskEras performs better than other whisker-tracking algorithms on several metrics. On our four video segments, WhiskEras detected more whiskers per frame than the Biotact Whisker Tracking Tool. The signal-to-noise ratio of the output of WhiskEras was higher than that of Janelia Whisk. As a result, the correlation between reflexive whisker movements and cerebellar Purkinje cell activity appeared to be stronger than previously found using other tracking algorithms. We conclude that WhiskEras facilitates the study of sensorimotor integration by markedly improving the accuracy of whisker tracking in untrimmed mice.
AB - Rodents engage in active touch using their facial whiskers: they explore their environment by making rapid back-and-forth movements. The fast nature of whisker movements, during which whiskers often cross each other, makes it notoriously difficult to track individual whiskers of the intact whisker field. We present here a novel algorithm, WhiskEras, for tracking of whisker movements in high-speed videos of untrimmed mice, without requiring labeled data. WhiskEras consists of a pipeline of image-processing steps: first, the points that form the whisker centerlines are detected with sub-pixel accuracy. Then, these points are clustered in order to distinguish individual whiskers. Subsequently, the whiskers are parameterized so that a single whisker can be described by four parameters. The last step consists of tracking individual whiskers over time. We describe that WhiskEras performs better than other whisker-tracking algorithms on several metrics. On our four video segments, WhiskEras detected more whiskers per frame than the Biotact Whisker Tracking Tool. The signal-to-noise ratio of the output of WhiskEras was higher than that of Janelia Whisk. As a result, the correlation between reflexive whisker movements and cerebellar Purkinje cell activity appeared to be stronger than previously found using other tracking algorithms. We conclude that WhiskEras facilitates the study of sensorimotor integration by markedly improving the accuracy of whisker tracking in untrimmed mice.
KW - algorithm
KW - cerebellum
KW - machine learning
KW - mouse
KW - object tracking
KW - Purkinje cell
KW - sensorimotor integration
KW - whiskers
UR - http://www.scopus.com/inward/record.url?scp=85097175948&partnerID=8YFLogxK
U2 - 10.3389/fncel.2020.588445
DO - 10.3389/fncel.2020.588445
M3 - Article
AN - SCOPUS:85097175948
SN - 1662-5102
VL - 14
SP - 1
EP - 18
JO - Frontiers in Cellular Neuroscience
JF - Frontiers in Cellular Neuroscience
M1 - 588445
ER -