1Ikerbasque, Basque Foundation for Science, Bilbao 48013, Spain2Computer Science and Artificial Intelligence Department, Basque Country University, San Sebastian 20018, Spain3Donostia International Physics Center, San Sebastian 20018, Spain4Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA5Laboratory for Optical and Computational Instrumentation, University of Wisconsin, Madison, WI 53706, USA6Howard Hughes Medical Institute, Janelia Research Campus, Ashburn, VA 20147, USA7Neuroscience Institute and Computer Science Department, Princeton University, NJ 08544, USA
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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:firstname.lastname@example.orgSupplementary information:Supplementary data are available at Bioinformatics online.