title: Trainable Weka Segmentation: a machine learning tool for microscopy pixel classification.
abstract: CONCLUSIONS
  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.
  
  
  METHODS
  TWS is distributed as open-source software as part of the Fiji image processing distribution of ImageJ at http://imagej.net/Trainable_Weka_Segmentation .
  
  
  BACKGROUND
  ignacio.arganda@ehu.eus.
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Ikerbasque, Basque Foundation for Science, Bilbao 48013, Spain.|Computer Science and Artificial Intelligence Department, Basque Country University, San Sebastian 20018, Spain.|Donostia International Physics Center, San Sebastian 20018, Spain.
authors.forename: Ignacio
authors.initials: I
authors.lastname: Arganda-Carreras
authors.affiliation: Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA.
authors.forename: Verena
authors.initials: V
authors.lastname: Kaynig
authors.affiliation: Laboratory for Optical and Computational Instrumentation, University of Wisconsin, Madison, WI 53706, USA.
authors.forename: Curtis
authors.initials: C
authors.lastname: Rueden
authors.affiliation: Laboratory for Optical and Computational Instrumentation, University of Wisconsin, Madison, WI 53706, USA.
authors.forename: Kevin W
authors.initials: KW
authors.lastname: Eliceiri
authors.affiliation: Laboratory for Optical and Computational Instrumentation, University of Wisconsin, Madison, WI 53706, USA.
authors.forename: Johannes
authors.initials: J
authors.lastname: Schindelin
authors.affiliation: Howard Hughes Medical Institute, Janelia Research Campus, Ashburn, VA 20147, USA.
authors.forename: Albert
authors.initials: A
authors.lastname: Cardona
authors.affiliation: Neuroscience Institute and Computer Science Department, Princeton University, NJ 08544, USA.
authors.forename: H
authors.initials: H
authors.lastname: Sebastian Seung
mesh_terms: D000818:Animals; D004330:Drosophila / Q000033:anatomy & histology / Q000648:ultrastructure; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D000069550:Machine Learning; D008853:Microscopy / Q000379:methods*; D012984:Software