We present an automated method for the tracking and dynamics

modeling of microtubules -a major component of the

cytoskeleton- which provides researchers with a previously

unattainable level of data analysis and quantification capabilities.

The proposed method improves upon the manual

tracking and analysis techniques by i) increasing accuracy

and quantified sample size in data collection, ii) eliminating

user bias and standardizing analysis, iii) making available

new features that are impractical to capture manually,

iv) enabling statistical extraction of dynamics patterns from

cellular processes, and v) greatly reducing required time

for entire studies. An automated procedure is proposed to

track each resolvable microtubule, whose aggregate activity

is then modeled by mixtures of Hidden Markov Models

to uncover dynamics patterns of underlying cellular and

experimental conditions. Our results support manually established

findings on an actual microtubule dataset and illustrate

how automated analysis of spatial and temporal

patterns offers previously unattainable insights to cellular

processes.

In  CVPR

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