Probability-based particle detection that enables threshold-free and robust in vivo single-molecule tracking

Journal article (2015)

Authors

C.S. Smith ImPhys/Quantitative Imaging , University of Massachusetts Medical School
S. Stallinga ImPhys/Quantitative Imaging
B. Rieger ImPhys/Quantitative Imaging
D. Grunwald University of Massachusetts Medical School
Research Group
ImPhys/Quantitative Imaging
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Published Date

2015

Language

English

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Research Group

ImPhys/Quantitative Imaging

Abstract

Single-molecule detection in fluorescence nanoscopy has become a powerful tool in cell biology but can present vexing issues in image analysis, such as limited signal, unspecific background, empirically set thresholds, image filtering, and false-positive detection limiting overall detection efficiency. Here we present a framework in which expert knowledge and parameter tweaking are replaced with a probability-based hypothesis test. Our method delivers robust and threshold-free signal detection with a defined error estimate and improved detection of weaker signals. The probability value has consequences for downstream data analysis, such as weighing a series of detections and corresponding probabilities, Bayesian propagation of probability, or defining metrics in tracking applications. We show that the method outperforms all current approaches, yielding a detection efficiency of >70% and a false-positive detection rate of