Trainable Weka Segmentation: a machine learning tool for microscopy pixel classification
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- Ignacio Arganda-Carreras
- Ikerbasque, Basque Foundation for Science, Bilbao, Spain
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- Verena Kaynig
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
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- Curtis Rueden
- Laboratory for Optical and Computational Instrumentation, University of Wisconsin, Madison, WI, USA
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- Kevin W Eliceiri
- Laboratory for Optical and Computational Instrumentation, University of Wisconsin, Madison, WI, USA
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- Johannes Schindelin
- Laboratory for Optical and Computational Instrumentation, University of Wisconsin, Madison, WI, USA
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- Albert Cardona
- Howard Hughes Medical Institute, Janelia Research Campus, Ashburn, VA, USA
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- H Sebastian Seung
- Neuroscience Institute and Computer Science Department, Princeton University, NJ, USA
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- Robert Murphy
- editor
Abstract
<jats:title>Abstract</jats:title> <jats:sec> <jats:title>Summary</jats:title> <jats:p>State-of-the-art light and electron microscopes are capable of acquiring large image datasets, but quantitatively evaluating the data often involves manually annotating structures of interest. This process is time-consuming and often a major bottleneck in the evaluation pipeline. To overcome this problem, we have introduced the Trainable Weka Segmentation (TWS), a machine learning tool that leverages a limited number of manual annotations in order to train a classifier and segment the remaining data automatically. In addition, TWS can provide unsupervised segmentation learning schemes (clustering) and can be customized to employ user-designed image features or classifiers.</jats:p> </jats:sec> <jats:sec> <jats:title>Availability and Implementation</jats:title> <jats:p>TWS is distributed as open-source software as part of the Fiji image processing distribution of ImageJ at http://imagej.net/Trainable_Weka_Segmentation.</jats:p> </jats:sec> <jats:sec> <jats:title>Supplementary information</jats:title> <jats:p>Supplementary data are available at Bioinformatics online.</jats:p> </jats:sec>
Journal
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- Bioinformatics
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Bioinformatics 33 (15), 2424-2426, 2017-03-30
Oxford University Press (OUP)
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Details 詳細情報について
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- CRID
- 1360855568981801600
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- ISSN
- 13674811
- 13674803
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- Data Source
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- Crossref