Analysis Of Neoplastic Lesions In Magnetic Resonance Imaging Using Self-organizing Maps

dc.creatorMei
dc.creatorPaulo Afonso; Carneiro
dc.creatorCleyton de Carvalho; Fraser
dc.creatorStephen J.; Min
dc.creatorLi Li; Reis
dc.creatorFabiano
dc.date2015-DEC
dc.date2016-06-07T13:35:58Z
dc.date2016-06-07T13:35:58Z
dc.date.accessioned2018-03-29T01:51:24Z
dc.date.available2018-03-29T01:51:24Z
dc.identifier
dc.identifierAnalysis Of Neoplastic Lesions In Magnetic Resonance Imaging Using Self-organizing Maps. Elsevier Science Bv, v. 359, p. 78-83 DEC-2015.
dc.identifier0022-510X
dc.identifierWOS:000367276200015
dc.identifier10.1016/j.jns.2015.10.032
dc.identifierhttp://www.jns-journal.com/article/S0022-510X(15)02524-1/abstract
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/244229
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1307927
dc.descriptionObjective: To provide an improved method for the identification and analysis of brain tumors in MRI scans using a semi-automated computational approach, that has the potential to provide a more objective, precise and quantitatively rigorous analysis, compared to human visual analysis. Background: Self-Organizing Maps (SUM) is an unsupervised, exploratory data analysis tool, which can automatically domain an image into selfsimilar regions or clusters, based on measures of similarity. It can be used to perform image-domain of brain tissue on MR images, without prior knowledge. Design/Methods: We used SUM to analyze T1,T2 and FLAIR acquisitions from two MM machines in our service from 14 patients with brain tumors confirmed by biopsies - three lymphomas, six glioblastomas, one meningioma, one ganglioglioma, two oligoastrocytomas and one astrocytoma. The SUM software was used to analyze the data from the three image acquisitions from each patient and generated a self-organized map for each containing 25 clusters. Results: Damaged tissue was separated from the normal tissue using the SUM technique. Furthermore, in some cases it allowed to separate different areas from within the tumor - like edema/peritumoral infiltration and necrosis. In lesions with less precise boundaries in FLAIR, the estimated damaged tissue area in the resulting map appears bigger. Conclusions: Our results showed that SUM has the potential to be a powerful MR imaging analysis technique for the assessment of brain tumors. (C) 2015 Published by Elsevier B.V.
dc.description359
dc.description1/Fev
dc.description
dc.description78
dc.description83
dc.description
dc.description
dc.description
dc.languageen
dc.publisherELSEVIER SCIENCE BV
dc.publisher
dc.publisherAMSTERDAM
dc.relationJOURNAL OF THE NEUROLOGICAL SCIENCES
dc.rightsembargo
dc.sourceWOS
dc.subjectClinical Neurology
dc.subjectNeurosciences
dc.titleAnalysis Of Neoplastic Lesions In Magnetic Resonance Imaging Using Self-organizing Maps
dc.typeArtículos de revistas


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