@article {48367, title = {Trainable Weka Segmentation: a machine learning tool for microscopy pixel classification.}, journal = {Bioinformatics (Oxford, England)}, volume = {33}, year = {2017}, month = {2017 Mar 29}, pages = {2424-6}, 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 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.

}, issn = {1367-4811}, doi = {10.1093/bioinformatics/btx180}, author = {Arganda-Carreras, Ignacio and Kaynig, Verena and Rueden, Curtis and Eliceiri, Kevin W and Schindelin, Johannes and Cardona, Albert and Seung, H Sebastian} } @article {5634, title = {Fiji: an open-source platform for biological-image analysis.}, journal = {Nature Methods}, volume = {9}, year = {2012}, month = {2012 Jul}, pages = {676-82}, abstract = {

Fiji is a distribution of the popular open-source software ImageJ focused on biological-image analysis. Fiji uses modern software engineering practices to combine powerful software libraries with a broad range of scripting languages to enable rapid prototyping of image-processing algorithms. Fiji facilitates the transformation of new algorithms into ImageJ plugins that can be shared with end users through an integrated update system. We propose Fiji as a platform for productive collaboration between computer science and biology research communities.

}, keywords = {Algorithms, Animals, Brain, Computational Biology, Computer-Assisted, Drosophila melanogaster, Image Enhancement, Image Processing, Imaging, Information Dissemination, Software, Software Design, Three-Dimensional}, issn = {1548-7105}, doi = {10.1038/nmeth.2019}, author = {Schindelin, Johannes and Arganda-Carreras, Ignacio and Frise, Erwin and Kaynig, Verena and Longair, Mark and Pietzsch, Tobias and Preibisch, Stephan and Rueden, Curtis and Saalfeld, Stephan and Schmid, Benjamin and Tinevez, Jean-Yves and White, Daniel James and Hartenstein, Volker and Eliceiri, Kevin and Tomancak, Pavel and Cardona, Albert} }