@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 {4984, title = {Biological imaging software tools.}, journal = {Nature Methods}, volume = {9}, year = {2012}, month = {2012 Jun 28}, pages = {697-710}, abstract = {

Few technologies are more widespread in modern biological laboratories than imaging. Recent advances in optical technologies and instrumentation are providing hitherto unimagined capabilities. Almost all these advances have required the development of software to enable the acquisition, management, analysis and visualization of the imaging data. We review each computational step that biologists encounter when dealing with digital images, the inherent challenges and the overall status of available software for bioimage informatics, focusing on open-source options.

}, issn = {1548-7105}, doi = {10.1038/nmeth.2084}, author = {Eliceiri, Kevin W and Berthold, Michael R and Goldberg, Ilya G and Ib{\'a}{\~n}ez, Luis and Manjunath, B S and Martone, Maryann E and Murphy, Robert F and Peng, Hanchuan and Plant, Anne L and Roysam, Badrinath and Stuurmann, Nico and Swedlow, Jason R and Tomancak, Pavel and Carpenter, Anne E.} }