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dc.contributor.authorÁlvarez Illán, Ignacio
dc.contributor.authorRamírez Pérez De Inestrosa, Javier 
dc.contributor.authorGorriz Sáez, Juan Manuel 
dc.contributor.authorMarino, Maria Adele
dc.contributor.authorAvendano, Daly
dc.contributor.authorHelbich, Thomas
dc.contributor.authorBaltzer, Pascal
dc.contributor.authorPinker, Katja
dc.contributor.authorMeyer-Baese, Anke
dc.date.accessioned2019-10-09T12:03:27Z
dc.date.available2019-10-09T12:03:27Z
dc.date.issued2018-10-24
dc.identifier.citationIllan, I. A., Ramirez, J., Gorriz, J. M., Marino, M. A., Avendano, D., Helbich, T., ... & Meyer-Baese, A. (2018). Automated Detection and Segmentation of Nonmass-Enhancing Breast Tumors with Dynamic Contrast-Enhanced Magnetic Resonance Imaging. Contrast media & molecular imaging, 2018.es_ES
dc.identifier.urihttp://hdl.handle.net/10481/57274
dc.description.abstractNonmass-enhancing (NME) lesions constitute a diagnostic challenge in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of the breast. Computer-aided diagnosis (CAD) systems provide physicians with advanced tools for analysis, assessment, and evaluation that have a significant impact on the diagnostic performance. Here, we propose a new approach to address the challenge of NME lesion detection and segmentation, taking advantage of independent component analysis (ICA) to extract data-driven dynamic lesion characterizations. A set of independent sources was obtained from the DCE-MRI dataset of breast cancer patients, and the dynamic behavior of the different tissues was described by multiple dynamic curves, together with a set of eigenimages describing the scores for each voxel. A new test image is projected onto the independent source space using the unmixing matrix, and each voxel is classified by a support vector machine (SVM) that has already been trained with manually delineated data. A solution to the high false-positive rate problem is proposed by controlling the SVM hyperplane location, outperforming previously published approaches.es_ES
dc.description.sponsorshipEuropean Unions Horizon 2020 Research and Innovation Programme under the Marie Skodowska-Curie grant agreement No. 656886es_ES
dc.description.sponsorshipAustrian National Bank "Jubilaeumsfond" Project 16219es_ES
dc.description.sponsorship2020-Research and Innovation Framework Programme PHC-11-2015 667211-2es_ES
dc.description.sponsorshipSiemens Austriaes_ES
dc.description.sponsorshipNovomedes_ES
dc.description.sponsorshipGuerbet, Francees_ES
dc.description.sponsorshipNIH/NCI Cancer Center Support Grant P30CA008748es_ES
dc.language.isoenges_ES
dc.publisherCONTRAST MEDIA & MOLECULAR IMAGINGes_ES
dc.relation656886es_ES
dc.relation667211-2es_ES
dc.rightsAtribución 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectCOMPUTER-AIDED DIAGNOSISes_ES
dc.subjectDCE-MRI DATAes_ES
dc.subjectLESION SEGMENTATIONes_ES
dc.subjectCOMPONENT ANALYSISes_ES
dc.subjectCáncer es_ES
dc.titleAutomated Detection and Segmentation of Nonmass-Enhancing Breast Tumors with Dynamic Contrast-Enhanced Magnetic Resonance Imaginges_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.identifier.doi10.1155/2018/5308517


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Atribución 3.0 España
Except where otherwise noted, this item's license is described as Atribución 3.0 España