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

Ignacio Arganda-Carreras, Verena Kaynig, Curtis Rueden, Kevin W. Eliceiri, Johannes Schindelin, Albert Cardona, Hyunjune Sebastian Seung

Research output: Contribution to journalArticle

320 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)2424-2426
Number of pages3
JournalBioinformatics
Volume33
Issue number15
DOIs
StatePublished - Aug 1 2017

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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    Arganda-Carreras, I., Kaynig, V., Rueden, C., Eliceiri, K. W., Schindelin, J., Cardona, A., & Seung, H. S. (2017). Trainable Weka Segmentation: A machine learning tool for microscopy pixel classification. Bioinformatics, 33(15), 2424-2426. https://doi.org/10.1093/bioinformatics/btx180