title: Image manifold revealing for breast lesion segmentation in DCE-MRI.
abstract: Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is widely used for breast lesion differentiation. Manual segmentation in DCE-MRI is difficult and open to viewer interpretation. In this paper, an automatic segmentation method based on image manifold revealing was introduced to overcome the problems of the currently used method. First, high dimensional datasets were constructed from a dynamic image series. Next, an embedded image manifold was revealed in the feature image by nonlinear dimensionality reduction technique. In the last stage, k-means clustering was performed to obtain final segmentation results. The proposed method was applied in actual clinical cases and compared with the gold standard. Statistical analysis showed that the proposed method achieved an acceptable accuracy, sensitivity, and specificity rates.
authors.affiliation: Digital Medical Research Center, Fudan University, Shanghai, China.|Shanghai Key Lab of Medical Image Computing and Computer Assisted Intervention, Shanghai, China.
authors.forename: Liang
authors.initials: L
authors.lastname: Hu
authors.affiliation: Digital Medical Research Center, Fudan University, Shanghai, China.|Shanghai Key Lab of Medical Image Computing and Computer Assisted Intervention, Shanghai, China.
authors.forename: Zhaoning
authors.initials: Z
authors.lastname: Cheng
authors.affiliation: Digital Medical Research Center, Fudan University, Shanghai, China.|Shanghai Key Lab of Medical Image Computing and Computer Assisted Intervention, Shanghai, China.
authors.forename: Manning
authors.initials: M
authors.lastname: Wang
authors.affiliation: Digital Medical Research Center, Fudan University, Shanghai, China.|Shanghai Key Lab of Medical Image Computing and Computer Assisted Intervention, Shanghai, China.
authors.forename: Zhijian
authors.initials: Z
authors.lastname: Song
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000473:pathology*; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D000069550:Machine Learning; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
keywords: Breast lesion; DCE-MRI; dimensionality reduction; manifold learning; tumor segmentation