Trainable Weka Segmentation: a machine learning tool for microscopy pixel classification.

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Bioinformatics (Oxford, England). 2017 Mar 29;33(15):2424-6. doi: 10.1093/bioinformatics/btx180

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Trainable Weka Segmentation: a machine learning tool for microscopy pixel classification. Cardona Lab

Arganda-Carreras I, Kaynig V, Rueden C, Eliceiri KW, Schindelin J, Cardona A, Seung HS

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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.

PMID: 28369169 [PubMed - indexed for MEDLINE]

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Trainable Weka Segmentation: a machine learning tool for microscopy pixel classification.

Abstract

PMID: 28369169 [PubMed - indexed for MEDLINE]

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