Fast, accurate, robust and Open Source Brain Extraction Tool (OSBET)
| Autor: | P. Donnelly Kehoe, Rafael Namías, Juan P. D'Amato, J. Nagel |
|---|---|
| Rok vydání: | 2015 |
| Předmět: |
education.field_of_study
Geometric analysis Computer science Population Process (computing) Skullstripping INGENIERÍAS Y TECNOLOGÍAS computer.software_genre Magnetic Resonance Imaging Thresholding Task (project management) Sørensen–Dice coefficient Ciencias de la Computación e Información Metric (mathematics) Preprocessor Data mining education Ciencias de la Información y Bioinformática computer CIENCIAS NATURALES Y EXACTAS Ingeniería Médica Neuroscience |
| Zdroj: | 11th International Symposium on Medical Information Processing and Analysis. |
| ISSN: | 0277-786X |
| DOI: | 10.1117/12.2207834 |
| Popis: | The removal of non-brain regions in neuroimaging is a critical task to perform a favorable preprocessing. The skull-stripping depends on different factors including the noise level in the image, the anatomy of the subject being scanned and the acquisition sequence. For these and other reasons, an ideal brain extraction method should be fast, accurate, user friendly, open-source and knowledge based (to allow for the interaction with the algorithm in case the expected outcome is not being obtained), producing stable results and making it possible to automate the process for large datasets. There are already a large number of validated tools to perform this task but none of them meets the desired characteristics. In this paper we introduced an open source brain extraction tool (OSBET), composed of four steps using simple well-known operations such as: optimal thresholding, binary morphology, labeling and geometrical analysis, that aims to assemble all the desired features. We present an experiment comparing OSBET with other six state-of-the-art techniques against a publicly available dataset consisting of 40 T1-weighted 3D scans and their corresponding manually segmented images. OSBET gave both: a short duration with an excellent accuracy, getting the best Dice Coefficient metric. Further validation should be performed, for instance, in unhealthy population, to generalize its usage for clinical purposes. Fil: Namias, Rafael. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y Sistemas; Argentina. Universidad Nacional de Rosario; Argentina Fil: Donnelly Kehoe, Patricio Andres. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y Sistemas; Argentina. Universidad Nacional de Rosario; Argentina Fil: D'amato, Juan Pablo. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Grupo de Plasmas Densos Magnetizados; Argentina; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Nagel, J.. Instituto Gamma; Argentina |
| Databáze: | OpenAIRE |
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