Atlas-Based probabilistic fibroglandular tissue segmentation in breast MRI

Authors:
Shandong Wu;Susan Weinstein;Despina Kontos
Affiliations:
Computational Breast Imaging Group, Department of Radiology, University of Pennsylvania, Philadelphia, PA;Breast Imaging Section, Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA;Computational Breast Imaging Group, Department of Radiology, University of Pennsylvania, Philadelphia, PA
Venue:
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
Year:
2012

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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.