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Publication Date
1 Janelia Publications
Showing 1-1 of 1 resultsSummary: 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 processis 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 leveragesa limited number of manual annotations in order to train a classifier and segment the remaining dataautomatically. 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.