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

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

Author information

1
Ikerbasque, Basque Foundation for Science, Bilbao 48013, Spain.
2
Computer Science and Artificial Intelligence Department, Basque Country University, San Sebastian 20018, Spain.
3
Donostia International Physics Center, San Sebastian 20018, Spain.
4
Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA.
5
Laboratory for Optical and Computational Instrumentation, University of Wisconsin, Madison, WI 53706, USA.
6
Howard Hughes Medical Institute, Janelia Research Campus, Ashburn, VA 20147, USA.
7
Neuroscience Institute and Computer Science Department, Princeton University, NJ 08544, USA.

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.

PMID:
28369169
DOI:
10.1093/bioinformatics/btx180
[Indexed for MEDLINE]

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