Segmentation methods for breast vasculature in dual-energy contrast-enhanced digital breast tomosynthesis

Kristen C. Lau; Hyo Min Lee; Tanushriya Singh; Andrew D. A. Maidment

doi:10.1117/12.2081031

23 April 2015 Segmentation methods for breast vasculature in dual-energy contrast-enhanced digital breast tomosynthesis

Kristen C. Lau, Hyo Min Lee, Tanushriya Singh, Andrew D. A. Maidment

Proceedings Volume 9412, Medical Imaging 2015: Physics of Medical Imaging; 941227 (2015) https://doi.org/10.1117/12.2081031
Event: SPIE Medical Imaging, 2015, Orlando, Florida, United States

Abstract

Dual-energy contrast-enhanced digital breast tomosynthesis (DE CE-DBT) uses an iodinated contrast agent to image the three-dimensional breast vasculature. The University of Pennsylvania has an ongoing DE CE-DBT clinical study in patients with known breast cancers. The breast is compressed continuously and imaged at four time points (1 pre-contrast; 3 post-contrast). DE images are obtained by a weighted logarithmic subtraction of the high-energy (HE) and low-energy (LE) image pairs. Temporal subtraction of the post-contrast DE images from the pre-contrast DE image is performed to analyze iodine uptake. Our previous work investigated image registration methods to correct for patient motion, enhancing the evaluation of vascular kinetics. In this project we investigate a segmentation algorithm which identifies blood vessels in the breast from our temporal DE subtraction images. Anisotropic diffusion filtering, Gabor filtering, and morphological filtering are used for the enhancement of vessel features. Vessel labeling methods are then used to distinguish vessel and background features successfully. Statistical and clinical evaluations of segmentation accuracy in DE-CBT images are ongoing.

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Kristen C. Lau, Hyo Min Lee, Tanushriya Singh, and Andrew D. A. Maidment "Segmentation methods for breast vasculature in dual-energy contrast-enhanced digital breast tomosynthesis", Proc. SPIE 9412, Medical Imaging 2015: Physics of Medical Imaging, 941227 (23 April 2015); https://doi.org/10.1117/12.2081031

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KEYWORDS

Image segmentation

Breast

Blood vessels

Image filtering

Digital filtering

Anisotropic filtering

Tissues

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