Proof-reading guidance in cell tracking by sampling from tracking-by-assignment models

Automated cell tracking methods are still error-prone. On very large data sets, uncertainty measures are thus needed to guide the expert to the most ambiguous events so these can be corrected with minimal effort. We present two easy-to-use methods to sample multiple proposal solutions from a trackin...

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Bibliographic Details
Main Authors: Schiegg, Martin, Heuer, Ben, Haubold, Carsten, Wolf, Steffen, Koethe, Ullrich, Hamprecht, Fred A.
Format: Conference Proceeding
Language:English
Subjects:
Biomedical measurement
Cell tracking
Gaussian processes
Graphical models
Labeling
machine learning
Measurement uncertainty
probabilistic graphical models
Proposals
Uncertainty
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Summary:Automated cell tracking methods are still error-prone. On very large data sets, uncertainty measures are thus needed to guide the expert to the most ambiguous events so these can be corrected with minimal effort. We present two easy-to-use methods to sample multiple proposal solutions from a tracking-by-assignment graphical model and experimentally evaluate the benefits of the uncertainty measures derived. Expert time for proof-reading is reduced greatly compared to random selection of predicted events.
ISSN:1945-7928
1945-8452
DOI:10.1109/ISBI.2015.7163895