title: Computerized breast lesions detection using kinetic and morphologic analysis for dynamic contrast-enhanced MRI.
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.
authors.affiliation: Department of Medical Imaging, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan.
authors.forename: Yeun-Chung
authors.initials: YC
authors.lastname: Chang
authors.affiliation: Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan.
authors.forename: Yan-Hao
authors.initials: YH
authors.lastname: Huang
authors.affiliation: Department of Surgery, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan.
authors.forename: Chiun-Sheng
authors.initials: CS
authors.lastname: Huang
authors.affiliation: Tu & Yuen Center for Functional Onco-Imaging, Department of Radiological Sciences, University of California, Irvine, CA; Department of Radiology, E-Da Hospital and I-Shou University, Kaohsiung, Taiwan.
authors.forename: Jeon-Hor
authors.initials: JH
authors.lastname: Chen
authors.affiliation: Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan; Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan. Electronic address: rfchang@csie.ntu.edu.tw.
authors.forename: Ruey-Feng
authors.initials: RF
authors.lastname: Chang
mesh_terms: D000328:Adult; D000368:Aged; D001943:Breast Neoplasms / Q000175:diagnosis*; D003198:Computer Simulation; D003287:Contrast Media / Q000493:pharmacokinetics; D005260:Female; D019786:Gadolinium DTPA / Q000493:pharmacokinetics*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007700:Kinetics; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008875:Middle Aged; D008954:Models, Biological; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
chemical_list: D003287:Contrast Media; C064925:gadodiamide; D019786:Gadolinium DTPA
keywords: Breast; DCE-MRI; Detection; Kinetic; Morphologic