Fully-automated fibroglandular tissue segmentation and volumetric density estimation in breast MRI by integrating a continuous max-flow model and a likelihood atlas

Shandong Wu; Susan P. Weinstein; Emily F. Conant; Despina Kontos

doi:10.1117/12.2007622

26 February 2013 Fully-automated fibroglandular tissue segmentation and volumetric density estimation in breast MRI by integrating a continuous max-flow model and a likelihood atlas

Shandong Wu, Susan P. Weinstein, Emily F. Conant, Despina Kontos

Author Affiliations +

Proceedings Volume 8670, Medical Imaging 2013: Computer-Aided Diagnosis; 86701C (2013) https://doi.org/10.1117/12.2007622
Event: SPIE Medical Imaging, 2013, Lake Buena Vista (Orlando Area), Florida, United States

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.

Citation Download Citation

Shandong Wu, Susan P. Weinstein, Emily F. Conant, and Despina Kontos "Fully-automated fibroglandular tissue segmentation and volumetric density estimation in breast MRI by integrating a continuous max-flow model and a likelihood atlas", Proc. SPIE 8670, Medical Imaging 2013: Computer-Aided Diagnosis, 86701C (26 February 2013); https://doi.org/10.1117/12.2007622

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