Automatic segmentation of fibroglandular tissue in breast MRI using anatomy-driven three-dimensional spatial context

Dong Wei; Susan Weinstein; Meng-Kang Hsieh; Lauren Pantalone; Despina Kontos

doi:10.1117/12.2292483

22 March 2018 Automatic segmentation of fibroglandular tissue in breast MRI using anatomy-driven three-dimensional spatial context

Dong Wei, Susan Weinstein, Meng-Kang Hsieh, Lauren Pantalone, Despina Kontos

Author Affiliations +

Proceedings Volume 10574, Medical Imaging 2018: Image Processing; 105742X (2018) https://doi.org/10.1117/12.2292483
Event: SPIE Medical Imaging, 2018, Houston, Texas, United States

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.

Citation Download Citation

Dong Wei, Susan Weinstein, Meng-Kang Hsieh, Lauren Pantalone, and Despina Kontos "Automatic segmentation of fibroglandular tissue in breast MRI using anatomy-driven three-dimensional spatial context", Proc. SPIE 10574, Medical Imaging 2018: Image Processing, 105742X (22 March 2018); https://doi.org/10.1117/12.2292483

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