Trainable Weka Segmentation: a machine learning tool for microscopy pixel classification.
Bioinformatics
; 33(15): 2424-2426, 2017 Aug 01.
Article
en En
| MEDLINE
| ID: mdl-28369169
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 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.
Texto completo:
1
Bases de datos:
MEDLINE
Asunto principal:
Procesamiento de Imagen Asistido por Computador
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Programas Informáticos
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Aprendizaje Automático
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Microscopía
Límite:
Animals
Idioma:
En
Revista:
Bioinformatics
Asunto de la revista:
INFORMATICA MEDICA
Año:
2017
Tipo del documento:
Article
País de afiliación:
España