The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Trainable Weka Segmentation: a machine learning tool for microscopy pixel classification
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.
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“Weka, an open-source software tool that enables users to perform ML-based image segmentation, object classification, and feature extraction with minimal programming experience,33-35 has been widely used in biological and clinical research to quantitatively analyze various types of microorganisms, cells, and tissues.36-40”
From SEMTWIST Quantification of Biofilm Infection in Human Chronic Wound Using Scanning Electron Microscopy and Machine Learning (2025) · cites this paper as [40]
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Cites
Fiji: an open-source platform for biological-image analysis
Schindelin, J., Arganda-Carreras, I., Frise, E., Kaynig, V., Longair, M., Pietzsch, T., Preibisch, S., Rueden, C., Saalfeld, S., Schmid, B., et al. (2012) · Nature Methods · doi:10.1038/nmeth.2019 · Cited by 66,890
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Research works printed but not linked: 4
- Dietz, 2016, Focus on Bio-Image Informatics, 179, 10.1007/978-3-319-28549-8_7 · DOI
- Hall, 2009, The weka data mining software: an update, ACM SIGKDD Explor. Newslett, 11, 10, 10.1145/1656274.1656278 · DOI · Paper page
- Kamentsky, 2011, Improved structure, function and compatibility for cellprofiler: modular high-throughput image analysis software, Bioinformatics, 27, 1179, 10.1093/bioinformatics/btr095 · DOI
- Marée, 2016, Collaborative analysis of multi-gigapixel imaging data using cytomine, Bioinformatics, 32, 1395, 10.1093/bioinformatics/btw013 · DOI
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Metadata
- Authors
- Arganda-Carreras, I., Kaynig, V., Rueden, C., Eliceiri, K. W., Schindelin, J., Cardona, A., & Sebastian Seung, H.
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2017-03-30
- Volume
- 33
- Issue
- 15
- Pages
- 2424-2426
- ISSN
- 1367-4803
- 1367-4811
- Cited by
- 2,424
- References
- 1
- Language
- en
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- The paper
- 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. https://doi.org/10.1093/bioinformatics/btx180
