Automated single particle detection and tracking for large microscopy datasets.

ABSTRACT: Recent advances in optical microscopy have enabled the acquisition of very large datasets from living cells with unprecedented spatial and temporal resolutions. Our ability to process these datasets now plays an essential role in order to understand many biological processes. In this paper, we present an automated particle detection algorithm capable of operating in low signal-to-noise fluorescence microscopy environments and handling large datasets. When combined with our particle linking framework, it can provide hitherto intractable quantitative measurements describing the dynamics of large cohorts of cellular components from organelles to single molecules. We begin with validating the performance of our method on synthetic image data, and then extend the validation to include experimen

SUBMITTER: Wilson RS 

PROVIDER: S-EPMC4892463 | biostudies-literature | 2016 May

REPOSITORIES: biostudies-literature