Abstract
Automatic characterization of breast density can enable more personalized breast cancer screening work flows. In this work, we present a novel method to automatically characterize breast density in mammography images. Our method computes a volumetric density map and measures the relative volume of glandular tissue (VBD%). For critical cases when masking of small masses may be possible it additionally quantifies the masking effect of glandular tissue. VBD% and the masking risk combined provide a 4-point density score that correlates with the BI-RADS 5th edition guidelines. We evaluated our approach using a study with 32 radiologists and 2400 breast images (600 4-view FFDM exams). In a subset of 415 images identified as critical cases the accuracy to detect dense breasts (density categories c or d) increased as shown by the area under the curves (0.783 vs. 0.621). By taking masking risk into consideration our method provides a more comprehensive assessment of breast density.
A. Fieselmann—The concepts and information presented in this paper are based on research and are not commercially available.
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Acknowlegements
The authors would like to thank Magnus Dustler, Pontus Timberg and Sophia Zackrisson for supporting evaluation of the methods using data from the Malmö Breast Tomosynthesis Screening Trial (MBTST).
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Fieselmann, A., Jerebko, A.K., Mertelmeier, T. (2016). Volumetric Breast Density Combined with Masking Risk: Enhanced Characterization of Breast Density from Mammography Images. In: Tingberg, A., Lång, K., Timberg, P. (eds) Breast Imaging. IWDM 2016. Lecture Notes in Computer Science(), vol 9699. Springer, Cham. https://doi.org/10.1007/978-3-319-41546-8_61
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DOI: https://doi.org/10.1007/978-3-319-41546-8_61
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