Automated non-mass enhancing lesion detection and segmentation in breast DCE-MRI

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 for the specific problem of NME detection and segmentation, by taking advantage of independent component analysis (ICA) to extract a data-driven dynamic characterization of tissue. A set of independent sources was obtained from a dataset of 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. The CAD system is trained and validated, reaching a DSC coefficient of 0.7215 for NME segmentation.