Automated analysis of non-mass-enhancing lesions in breast MRI based on morphological, kinetic, and spatio-temporal moments and joint segmentation-motion compensation technique

Abstract

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) represents an established method for the detection and diagnosis of breast lesions. While mass-like enhancing lesions can be easily categorized according to the Breast Imaging Reporting and Data System (BI-RADS) MRI lexicon, a majority of diagnostically challenging lesions, the so called non-mass-like enhancing lesions, remain both qualitatively as well as quantitatively difficult to analyze. Thus, the evaluation of kinetic and/or morphological characteristics of non-masses represents a challenging task for an automated analysis and is of crucial importance for advancing current computer-aided diagnosis (CAD) systems. Compared to the well-characterized mass-enhancing lesions, non-masses have no well-defined and blurred tumor borders and a kinetic behavior that is not easily generalizable and thus discriminative for malignant and benign non-masses. To overcome these difficulties and pave the way for novel CAD systems for non-masses, we will evaluate several kinetic and morphological descriptors separately and a novel technique, the Zernike velocity moments, to capture the joint spatio-temporal behavior of these lesions, and additionally consider the impact of non-rigid motion compensation on a correct diagnosis.

Characterization of Suspicious Breast Lesions with Dynamic Contrast enhanced MRI In comparison to Conventional mammography and Ultrasonography

“…Morphological criteria have also been identified as valuable diagnostic tools. Recently, combinations of different dynamic and morphological characteristics have been reported that can reach diagnostic sensitivities up to 97% and specificities up to 76.5% [21].…”

“…Morphological criteria have also been identified as valuable diagnostic tools. Recently, combinations of different dynamic and morphological characteristics have been reported that can reach diagnostic sensitivities up to 97% and specificities up to 76.5% [21].In our study we combined both morphologic and dynamic parameters and its modification into BI-RADS category for lesion classification. The sensitivity of MRI examinations was 98.6% while the specificity was78.8%, this was attributed to the small number of the benign lesions, representing 7 (19.4%) out of 36 examined lesions.…”

Efficacy of Magnetic Resonance Imaging in BIRADS 3, 4 and 5 Patients Detected on Full Filed Digital Mammography: Our Experience

“…As per ACR BIRADS LEXICON, there are certain morphological and dynamic contrast enhancement characteristics for assessing lesions on MRI. Combinations of different dynamic and morphological characteristics have been reported that can reach diagnostic sensitivities up to 97% and specificities up to 76.5% [13]. In our study we combined both morphologic and dynamic parameters and its modification into BIRADS category for lesion classification.…”

Segmentation and Kinetic Analysis of Breast Lesions in DCE-MR Imaging Using ICA

“…Also semi-automatic lesion extraction is performed by threshold based segmentation. Hoffmann et al [8] proposed a modification for the segmentation algorithm by Chan and Vese [2]. As comparison algorithm this method is included in our proposed workflow.…”

“…By stopping the curve the boundary of the objects is determined. Here, the following modifications proposed by Hoffmann [8] are applied: The contour to be found by the algorithm is set to a three dimensional function in order to evolve a three dimensional segmentation. The segmentation function is modified to achieve a smoother transition of the contour of the lesions which is defined by the newly introduced parameter α.…”