Automated detection and segmentation of nonmass-enhancing breast tumors with dynamic contrast-enhanced magnetic resonance imaging Journal Article

   


Authors: Illan, I. A.; Ramirez, J.; Gorriz, J. M.; Marino, M. A.; Avendano, D.; Helbich, T.; Baltzer, P.; Pinker, K.; Meyer-Baese, A.
Article Title: Automated detection and segmentation of nonmass-enhancing breast tumors with dynamic contrast-enhanced magnetic resonance imaging
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. © 2018 Ignacio Alvarez Illan et al.
Journal Title: Contrast Media and Molecular Imaging
Volume: 2018
ISSN: 1555-4309
Publisher: John Wiley & Sons  
Date Published: 2018-10-24
Start Page: 5308517
Language: English
DOI: 10.1155/2018/5308517
PUBMED: 30647551
PROVIDER: scopus
PMCID: PMC6311739
DOI/URL:

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85059871357&doi=10.1155%2f2018%2f5308517&partnerID=40&md5=f934fc67d174cb60a711044f913e9ba9
Notes: Article -- Export Date: 1 February 2019 -- Source: Scopus
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MSK Authors
  1. Katja Pinker-Domenig
    214 Pinker-Domenig
  2. Daly Betzabeth Avendano
    14 Avendano
  3. Maria Adele Marino
    16 Marino
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