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dc.contributor.authorChalupa, Danielcs
dc.contributor.authorMikulka, Jancs
dc.date.accessioned2020-08-04T11:01:11Z
dc.date.available2020-08-04T11:01:11Z
dc.date.issued2018-11-12cs
dc.identifier.citationSymmetry. 2018, vol. 10, issue 11, p. 1-9.en
dc.identifier.issn2073-8994cs
dc.identifier.other151184cs
dc.identifier.urihttp://hdl.handle.net/11012/137219
dc.description.abstractThe rather impressive extension library of medical image-processing platform 3D Slicer lacks a wide range of machine-learning toolboxes. The authors have developed such a toolbox that incorporates commonly used machine-learning libraries. The extension uses a simple graphical user interface that allows the user to preprocess data, train a classifier, and use that classifier in common medical image-classification tasks, such as tumor staging or various anatomical segmentations without a deeper knowledge of the inner workings of the classifiers. A series of experiments were carried out to showcase the capabilities of the extension and quantify the symmetry between the physical characteristics of pathological tissues and the parameters of a classifying model. These experiments also include an analysis of the impact of training vector size and feature selection on the sensitivity and specificity of all included classifiers. The results indicate that training vector size can be minimized for all classifiers. Using the data from the Brain Tumor Segmentation Challenge, Random Forest appears to have the widest range of parameters that produce sufficiently accurate segmentations, while optimal Support Vector Machines’ training parameters are concentrated in a narrow feature space.en
dc.formattextcs
dc.format.extent1-9cs
dc.format.mimetypeapplication/pdfcs
dc.language.isoencs
dc.publisherMDPIcs
dc.relation.ispartofSymmetrycs
dc.relation.urihttps://www.mdpi.com/2073-8994/10/11/627cs
dc.rightsCreative Commons Attribution 4.0 Internationalcs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subject3D sliceren
dc.subjectclassificationen
dc.subjectextensionen
dc.subjectrandom foresten
dc.subjectsegmentationen
dc.subjectsensitivity analysisen
dc.subjectsupport vector machineen
dc.subjecttumoren
dc.titleA Novel Tool for Supervised Segmentation Using 3D Sliceren
thesis.grantorVysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií. Ústav teoretické a experimentální elektrotechnikycs
sync.item.dbidVAV-151184en
sync.item.dbtypeVAVen
sync.item.insts2020.08.04 13:01:11en
sync.item.modts2020.08.04 12:41:10en
dc.coverage.issue11cs
dc.coverage.volume10cs
dc.identifier.doi10.3390/sym10110627cs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/2073-8994/cs
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen


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Creative Commons Attribution 4.0 International
Except where otherwise noted, this item's license is described as Creative Commons Attribution 4.0 International