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

Bioinformatics. 2017 Aug 1;33(15):2424-2426. doi: 10.1093/bioinformatics/btx180.


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 .


Supplementary information: Supplementary data are available at Bioinformatics online.

MeSH terms

  • Animals
  • Drosophila / anatomy & histology
  • Drosophila / ultrastructure
  • Image Processing, Computer-Assisted / methods*
  • Machine Learning*
  • Microscopy / methods*
  • Software*