Share Email Print

Proceedings Paper

Fully-automated fibroglandular tissue segmentation and volumetric density estimation in breast MRI by integrating a continuous max-flow model and a likelihood atlas
Author(s): Shandong Wu; Susan P. Weinstein; Emily F. Conant; Despina Kontos
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

Studies suggest that the relative amount of fibroglandular tissue in the breast as quantified in breast MRI can be predictive of the risk for developing breast cancer. Automated segmentation of the fibroglandular tissue from breast MRI data could therefore be an essential component in quantitative risk assessment. In this work we propose a new fullyautomated 3D segmentation algorithm, namely the continuous max-flow (CMF)-Atlas method, to estimate the volumetric amount of fibroglandular tissue in breast MRI. Our method goes through a first step of applying a continuous max-flow model in the MR image intensity space to produce an initial voxel-wise likelihood map of being fibroglandular tissue. Then we further incorporate an a-priori learned fibroglandular tissue likelihood atlas to refine the initial likelihood map to achieve enhanced segmentation, from which the relative (e.g., percent) volumetric amount of fibroglandular tissue (FT%) in the breast is computed. Our method is evaluated by a representative dataset of 16 3D bilateral breast MRI scans (32 breasts, 896 tomographic MR slices in total). A high correlation (r=0.95) is achieved in FT% estimation, and the overall averaged spatial segmentation agreement is 0.77 in terms of Dice’s coefficient, between the automated segmentation and the manual segmentation obtained from an experienced breast imaging radiologist. The automated segmentation method also runs time-efficiently at ~1 minute for each 3D MR scan (56 slices), compared to ~15 minutes needed for manual segmentation. Our method can serve as an effective tool for processing large scale clinical breast MR datasets for quantitative fibroglandular tissue estimation.

Paper Details

Date Published: 26 February 2013
PDF: 6 pages
Proc. SPIE 8670, Medical Imaging 2013: Computer-Aided Diagnosis, 86701C (26 February 2013); doi: 10.1117/12.2007622
Show Author Affiliations
Shandong Wu, Computational Breast Imaging Group, Univ. of Pennsylvania (United States)
Susan P. Weinstein, Hospital of the Univ. of Pennsylvania (United States)
Emily F. Conant, Hospital of the Univ. of Pennsylvania (United States)
Despina Kontos, Computational Breast Imaging Group, Univ. of Pennsylvania (United States)

Published in SPIE Proceedings Vol. 8670:
Medical Imaging 2013: Computer-Aided Diagnosis
Carol L. Novak; Stephen Aylward, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?