@misc{10481/57274,
year = {2018},
month = {10},
url = {http://hdl.handle.net/10481/57274},
abstract = {Nonmass-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.},
organization = {European Unions
Horizon 2020 Research and Innovation Programme under
the Marie Skodowska-Curie grant agreement No. 656886},
organization = {Austrian National Bank "Jubilaeumsfond" Project 	
16219},
organization = {2020-Research and Innovation Framework Programme PHC-11-2015 	
667211-2},
organization = {Siemens Austria},
organization = {Novomed},
organization = {Guerbet, France},
organization = {NIH/NCI Cancer Center Support Grant 	
P30CA008748},
publisher = {CONTRAST MEDIA & MOLECULAR IMAGING},
keywords = {COMPUTER-AIDED DIAGNOSIS},
keywords = {DCE-MRI DATA},
keywords = {LESION SEGMENTATION},
keywords = {COMPONENT ANALYSIS},
keywords = {Cáncer},
title = {Automated Detection and Segmentation of Nonmass-Enhancing Breast Tumors with Dynamic Contrast-Enhanced Magnetic Resonance Imaging},
doi = {10.1155/2018/5308517},
author = {Álvarez Illán, Ignacio and Ramírez Pérez De Inestrosa, Javier and Gorriz Sáez, Juan Manuel and Marino, Maria Adele and Avendano, Daly and Helbich, Thomas and Baltzer, Pascal and Pinker, Katja and Meyer-Baese, Anke},
}