Trainable Weka Segmentation: a machine learning tool for microscopy pixel classification - PubMed Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2017 Aug 1;33(15):2424-2426.
doi: 10.1093/bioinformatics/btx180.

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

Ignacio Arganda-Carreras  1   2   3 , Verena Kaynig  4 , Curtis Rueden  5 , Kevin W Eliceiri  5 , Johannes Schindelin  5 , Albert Cardona  6 , H Sebastian Seung  7
Affiliations

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

Ignacio Arganda-Carreras et al. Bioinformatics. .

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 .

Contact: ignacio.arganda@ehu.eus.

Supplementary information: Supplementary data are available at Bioinformatics online.

PubMed Disclaimer

Similar articles

See all similar articles

Cited by

See all "Cited by" articles