In this work we evaluate an approach for breast density assessment of digital breast tomosynthesis (DBT) data using the central projection image. A total of 348 random cases (both FFDM CC and MLO views and DBT MLO views) were collected using a Siemens Mammomat Inspiration tomosynthesis unit at Unilabs, Malmö. The cases underwent both BI-RADS 5th Edition labeling by radiologists and automated volumetric breast density analysis (VBDA) by an algorithm. Preliminary results showed an observed agreement of 70% (weighted Kappa, κ = 0.73) between radiologists and VBDA using FFDM images and 63% (κ = 0.62) for radiologists and VBDA using DBT images. Comparison between densities for FFDM and DBT resulted in high correlation (r = 0.94) and an observed agreement of 72% (κ = 0.76). The automated analysis is a promising approach using low dose central projection DBT images in order to get radiologist- like density ratings similar to results obtained from FFDM.
CITATION STYLE
Timberg, P., Fieselmann, A., Dustler, M., Petersson, H., Sartor, H., Lång, K., … Zackrisson, S. (2016). Breast density assessment using breast tomosynthesis images. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9699, pp. 197–202). Springer Verlag. https://doi.org/10.1007/978-3-319-41546-8_26
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