DOI: 10.1093/bioinformatics/btx180. Trainable Weka Segmentation: a machine learning tool for microscopy pixel classification

Trainable Weka Segmentation: a machine learning tool for microscopy pixel classification

10.1093/bioinformatics/btx180

Crossref journal-article

Oxford University Press (OUP)

Bioinformatics (286)

Abstract

Abstract Summary 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. Availability and Implementation TWS is distributed as open-source software as part of the Fiji image processing distribution of ImageJ at http://imagej.net/Trainable_Weka_Segmentation. Supplementary information Supplementary data are available at Bioinformatics online.

Bibliography

Arganda-Carreras, I., Kaynig, V., Rueden, C., Eliceiri, K. W., Schindelin, J., Cardona, A., & Sebastian Seung, H. (2017). Trainable Weka Segmentation: a machine learning tool for microscopy pixel classification. Bioinformatics, 33(15), 2424â2426.

Authors 7

Ignacio Arganda-Carreras (first)
Verena Kaynig (additional)
Curtis Rueden (additional)
Kevin W Eliceiri (additional)
Johannes Schindelin (additional)
Albert Cardona (additional)
H Sebastian Seung (additional)

References 7 Referenced 1,943

10.1007/978-3-319-28549-8_7 / Focus on Bio-Image Informatics by Dietz (2016)
10.1145/1656274.1656278 / ACM SIGKDD Explor. Newslett / The weka data mining software: an update by Hall (2009)
10.1093/bioinformatics/btr095 / Bioinformatics / Improved structure, function and compatibility for cellprofiler: modular high-throughput image analysis software by Kamentsky (2011)
{'key': '2023063012504785400_btx180-B4', 'author': 'Li', 'year': '2015'} by Li (2015)
10.1093/bioinformatics/btw013 / Bioinformatics / Collaborative analysis of multi-gigapixel imaging data using cytomine by Marée (2016)
10.1038/nmeth.2019 / Nat. Methods / Fiji: an open-source platform for biological-image analysis by Schindelin (2012)
{'key': '2023063012504785400_btx180-B7', 'author': 'Sommer', 'year': '2011'} by Sommer (2011)

Dates

Type	When
Created	8 years, 4 months ago (March 28, 2017, 10:04 a.m.)
Deposited	2 years, 1 month ago (June 30, 2023, 8:50 a.m.)
Indexed	10 minutes ago (Aug. 27, 2025, 8:07 a.m.)
Issued	8 years, 4 months ago (March 30, 2017)
Published	8 years, 4 months ago (March 30, 2017)
Published Online	8 years, 4 months ago (March 30, 2017)
Published Print	8 years ago (Aug. 1, 2017)

Funders 0

None

BibTeX

@article{Arganda_Carreras_2017, title={Trainable Weka Segmentation: a machine learning tool for microscopy pixel classification}, volume={33}, ISSN={1367-4811}, url={http://dx.doi.org/10.1093/bioinformatics/btx180}, DOI={10.1093/bioinformatics/btx180}, number={15}, journal={Bioinformatics}, publisher={Oxford University Press (OUP)}, author={Arganda-Carreras, Ignacio and Kaynig, Verena and Rueden, Curtis and Eliceiri, Kevin W and Schindelin, Johannes and Cardona, Albert and Sebastian Seung, H}, editor={Murphy, Robert}, year={2017}, month=mar, pages={2424–2426} }

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