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

Author

Schiegg, Martin ; Heuer, Ben ; Haubold, Carsten ; Wolf, Steffen ; Koethe, Ullrich ; Hamprecht, Fred A.

Author_Institution

HCI/IWR, Univ. of Heidelberg, Heidelberg, Germany

fYear

2015

fDate

16-19 April 2015

Firstpage

394

Lastpage

398

Abstract

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.

Keywords

cellular biophysics; graph theory; image sampling; learning (artificial intelligence); medical image processing; object tracking; random processes; automated cell tracking methods; cell sampling; multiple proposal solutions; predicted events; proof-reading guidance; random selection; tracking-by-assignment graphical model; tracking-by-assignment models; uncertainty measures; Biomedical measurement; Gaussian processes; Graphical models; Labeling; Measurement uncertainty; Proposals; Uncertainty; Cell tracking; machine learning; probabilistic graphical models; uncertainty;

fLanguage

English

Publisher

ieee

Conference_Titel

Biomedical Imaging (ISBI), 2015 IEEE 12th International Symposium on

Conference_Location

New York, NY

Type

conf

DOI

10.1109/ISBI.2015.7163895

Filename

7163895