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Bayesian image analysis with on-line confidence estimates and its application to microtubule tracking


Cardinale, Janick; Rauch, Alexander; Barral, Yves; Székely, Gábor; Sbalzarini, Ivo F (2009). Bayesian image analysis with on-line confidence estimates and its application to microtubule tracking. In: IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Boston, MA, 28 June 2009 - 1 July 2009, 1091-1094.

Abstract

Automated analysis of fluorescence microscopy data relies on robust segmentation and tracking algorithms for sub-cellular structures in order to generate quantitative results. The accuracy of the image processing results is, however, frequently unknown or determined a priori on synthetic benchmark data. We present a particle filter framework based on Markov Chain Monte Carlo methods and adaptive annealing. Our algorithm provides on-line per-frame estimates of the detection and tracking confidence at run time. We validate the accuracy of the estimates and apply the algorithm to tracking microtubules in mitotic yeast cells. This is based on a likelihood function that accounts for the dominant noise sources in the imaging equipment. The confidence estimates provided by the present algorithm allow on-line control of the detection and tracking quality.

Automated analysis of fluorescence microscopy data relies on robust segmentation and tracking algorithms for sub-cellular structures in order to generate quantitative results. The accuracy of the image processing results is, however, frequently unknown or determined a priori on synthetic benchmark data. We present a particle filter framework based on Markov Chain Monte Carlo methods and adaptive annealing. Our algorithm provides on-line per-frame estimates of the detection and tracking confidence at run time. We validate the accuracy of the estimates and apply the algorithm to tracking microtubules in mitotic yeast cells. This is based on a likelihood function that accounts for the dominant noise sources in the imaging equipment. The confidence estimates provided by the present algorithm allow on-line control of the detection and tracking quality.

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5 citations in Web of Science®
9 citations in Scopus®
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Additional indexing

Item Type:Conference or Workshop Item (Paper), refereed, original work
Communities & Collections:Special Collections > SystemsX.ch
Special Collections > SystemsX.ch > Research, Technology and Development Projects > LipidX
Special Collections > SystemsX.ch > Research, Technology and Development Projects > WingX
Dewey Decimal Classification:570 Life sciences; biology
Language:English
Event End Date:1 July 2009
Deposited On:05 Jul 2013 12:06
Last Modified:05 Apr 2016 16:51
Publisher DOI:https://doi.org/10.1109/ISBI.2009.5193246
Permanent URL: https://doi.org/10.5167/uzh-79217

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