title: Atlas-based probabilistic fibroglandular tissue segmentation in breast MRI.
abstract: In this paper we propose an atlas-aided probabilistic model-based segmentation method for estimating the fibroglandular tissue in breast MRI, where a novel fibroglandular tissue atlas is learned to aid the segmentation. The atlas represents a pixel-wise likelihood of being fibroglandular tissue in the breast, which is derived by combining deformable image warping, using aligned breast contour points as landmarks, with a kernel density estimation technique. A mixture multivariate model is learned to characterize the breast tissue using MR image features, and the segmentation is subsequently based on examining the posterior probability where the learned atlas is incorporated as the prior probability. In our experiments, the algorithm-generated segmentation results of 10 cases are compared to the manual segmentations, verified by an experienced breast imaging radiologist, to assess the accuracy of the algorithm, where the Dice's Similarity Coefficient (DSC) shows a 0.85 agreement. The proposed automated segmentation method could be used to estimate the volumetric amount of fibroglandular tissue in the breast for breast cancer risk estimation.
authors.affiliation: Computational Breast Imaging Group, Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA. shandong.wu@uphs.upenn.edu
authors.forename: Shandong
authors.initials: S
authors.lastname: Wu
authors.forename: Susan
authors.initials: S
authors.lastname: Weinstein
authors.forename: Despina
authors.initials: D
authors.lastname: Kontos
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000473:pathology*; D003198:Computer Simulation; D005260:Female; D005355:Fibrosis / Q000473:pathology; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008953:Models, Anatomic; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D011230:Precancerous Conditions / Q000473:pathology*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity