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Automatic segmentation of fibroglandular tissue in breast MRI using anatomy-driven three-dimensional spatial context
Author(s): Dong Wei; Susan Weinstein; Meng-Kang Hsieh; Lauren Pantalone; Despina Kontos
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Paper Abstract

The relative amount of fibroglandular tissue (FGT) in the breast has been shown to be a risk factor for breast cancer. However, automatic segmentation of FGT in breast MRI is challenging due mainly to its wide variation in anatomy (e.g., amount, location and pattern, etc.), and various imaging artifacts especially the prevalent bias-field artifact. Motivated by a previous work demonstrating improved FGT segmentation with 2-D a priori likelihood atlas, we propose a machine learning-based framework using 3-D FGT context. The framework uses features specifically defined with respect to the breast anatomy to capture spatially varying likelihood of FGT, and allows (a) intuitive standardization across breasts of different sizes and shapes, and (b) easy incorporation of additional information helpful to the segmentation (e.g., texture). Extended from the concept of 2-D atlas, our framework not only captures spatial likelihood of FGT in 3-D context, but also broadens its applicability to both sagittal and axial breast MRI rather than being limited to the plane in which the 2-D atlas is constructed. Experimental results showed improved segmentation accuracy over the 2-D atlas method, and demonstrated further improvement by incorporating well-established texture descriptors.

Paper Details

Date Published: 22 March 2018
PDF: 6 pages
Proc. SPIE 10574, Medical Imaging 2018: Image Processing, 105742X (22 March 2018); doi: 10.1117/12.2292483
Show Author Affiliations
Dong Wei, Perelman School of Medicine, Univ. of Pennsylvania (United States)
Susan Weinstein, Perelman School of Medicine, Univ. of Pennsylvania (United States)
Meng-Kang Hsieh, Perelman School of Medicine, Univ. of Pennsylvania (United States)
Lauren Pantalone, Perelman School of Medicine, Univ. of Pennsylvania (United States)
Despina Kontos, Perelman School of Medicine, Univ. of Pennsylvania (United States)


Published in SPIE Proceedings Vol. 10574:
Medical Imaging 2018: Image Processing
Elsa D. Angelini; Bennett A. Landman, Editor(s)

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