Computerized breast lesions detection using kinetic and morphologic analysis for dynamic contrast-enhanced MRI
Journal
Magnetic Resonance Imaging
Journal Volume
32
Journal Issue
5
Pages
514-522
Date Issued
2014
Author(s)
Abstract
To facilitate rapid and accurate assessment, this study proposed a novel fully automatic method to detect and identify focal tumor breast lesions using both kinetic and morphologic features from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). After motion registration of all phases of the DCE-MRI study, three automatically generated lines were used to segment the whole breast region of each slice. The kinetic features extracted from the pixel-based time-signal intensity curve (TIC) by a two-stage detection algorithm was first used, and then three-dimensional (3-D) morphologic characteristics of the detected regions were applied to differentiate between tumor and non-tumor regions. In this study, 95 biopsy-confirmed lesions (28 benign and 67 malignant lesions) in 54 women were used to evaluate the detection efficacy of the proposed system. The detection performance was analyzed using the free-response operating characteristics (FROC) curve and detection rate. The proposed computer-aided detection (CADe) system had a detection rate of 92.63% (88/95) of all tumor lesions, with 6.15 false positives per case. Based on the results, kinetic features extracted by TIC can be used to detect tumor lesions and 3-D morphology can effectively reduce the false positives. © 2014 Elsevier Inc.
Subjects
Breast; DCE-MRI; Detection; Kinetic; Morphologic
Other Subjects
adult; aged; article; breast lesion; breast tumor; computer assisted diagnosis; controlled study; diagnostic accuracy; diagnostic imaging; dynamic contrast enhanced magnetic resonance imaging; false positive result; female; human; image analysis; image display; major clinical study; morphometrics; nuclear magnetic resonance imaging; priority journal; automated pattern recognition; biological model; Breast Neoplasms; computer assisted diagnosis; computer simulation; diagnostic use; image enhancement; kinetics; middle aged; nuclear magnetic resonance imaging; procedures; reproducibility; sensitivity and specificity; contrast medium; gadodiamide; gadolinium pentetate; Adult; Aged; Breast Neoplasms; Computer Simulation; Contrast Media; Female; Gadolinium DTPA; Humans; Image Enhancement; Image Interpretation, Computer-Assisted; Kinetics; Magnetic Resonance Imaging; Middle Aged; Models, Biological; Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity
Publisher
Elsevier Inc.
Type
journal article