Trainable Weka Segmentation: a Machine Learning Tool for Microscopy Pixel Classification

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.