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Active structured learning for cell tracking: algorithm, framework, and usability

One distinguishing property of life is its temporal dynamics, and it is hence only natural that time lapse experiments play a crucial role in modern biomedical research areas such as signaling pathways, drug discovery or developmental biology. Such experiments yield a very large number of images tha...

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Hauptverfasser: Lou, Xinghua (VerfasserIn) , Schiegg, Martin (VerfasserIn) , Hamprecht, Fred (VerfasserIn)
Dokumenttyp: Article (Journal)
Sprache:Englisch
Veröffentlicht: January 02, 2014
In: IEEE transactions on medical imaging
Year: 2014, Jahrgang: 33, Heft: 4, Pages: 849-860
ISSN:1558-254X
DOI:10.1109/TMI.2013.2296937
Online-Zugang:Resolving-System, lizenzpflichtig, Volltext: https://doi.org/10.1109/TMI.2013.2296937
Verlag, lizenzpflichtig, Volltext: https://ieeexplore.ieee.org/document/6698356
Volltext
Verfasserangaben:Xinghua Lou, Martin Schiegg, and Fred A. Hamprecht

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520 |a One distinguishing property of life is its temporal dynamics, and it is hence only natural that time lapse experiments play a crucial role in modern biomedical research areas such as signaling pathways, drug discovery or developmental biology. Such experiments yield a very large number of images that encode complex cellular activities, and reliable automated cell tracking emerges naturally as a prerequisite for further quantitative analysis. However, many existing cell tracking methods are restricted to using only a small number of features to allow for manual tweaking. In this paper, we propose a novel cell tracking approach that embraces a powerful machine learning technique to optimize the tracking parameters based on user annotated tracks. Our approach replaces the tedious parameter tuning with parameter learning and allows for the use of a much richer set of complex tracking features, which in turn affords superior prediction accuracy. Furthermore, we developed an active learning approach for efficient training data retrieval, which reduces the annotation effort to only 17%. In practical terms, our approach allows life science researchers to inject their expertise in a more intuitive and direct manner. This process is further facilitated by using a glyph visualization technique for ground truth annotation and validation. Evaluation and comparison on several publicly available benchmark sequences show significant performance improvement over recently reported approaches. Code and software tools are provided to the public. 
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650 4 |a Artificial Intelligence 
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650 4 |a biology computing 
650 4 |a biomedical research areas 
650 4 |a Cell Line 
650 4 |a cell tracking 
650 4 |a cell tracking approach 
650 4 |a cellular biophysics 
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650 4 |a Computational Biology 
650 4 |a Data Curation 
650 4 |a Data models 
650 4 |a Databases, Factual 
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650 4 |a mitosis detection 
650 4 |a quantitative analysis 
650 4 |a signaling pathways 
650 4 |a Software 
650 4 |a structured prediction 
650 4 |a temporal dynamics 
650 4 |a time lapse experiments 
650 4 |a tracking features 
650 4 |a Training 
650 4 |a Training data 
650 4 |a training data retrieval 
650 4 |a Uncertainty 
650 4 |a user annotated tracks 
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