Automated detection and segmentation of non-mass enhancing breast tumors with dynamic contrast-enhanced magnetic resonance imaging

Ignacio Alvarez Illan; Javier Ramirez; Juan M. Gorriz; Maria Adele Marino; Daly Avendaño; Thomas Helbich; Pascal Baltzer; Katja Pinker; Anke Meyer-Baese

arXiv · 1803.04200

Automated detection and segmentation of non-mass enhancing breast tumors with dynamic contrast-enhanced magnetic resonance imaging

Ignacio Alvarez Illan·Javier Ramirez·Juan M. Gorriz·Maria Adele Marino·Daly Avendaño·Thomas Helbich·Pascal Baltzer·Katja Pinker·Anke Meyer-Baese

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Abstract

Non-mass enhancing lesions (NME) 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 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 DCE-MRI dataset of breast 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.

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eess.IV cs.CV

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Ignacio Alvarez Illan, Javier Ramirez, Juan M. Gorriz, Maria Adele Marino, Daly Avendaño, Thomas Helbich, Pascal Baltzer, Katja Pinker, Anke Meyer-Baese. 2018-03-12. Automated detection and segmentation of non-mass enhancing breast tumors with dynamic contrast-enhanced magnetic resonance imaging. https://arxiv.org/abs/1803.04200

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