Abstract #2743

Automated Breast MRI Segmentation Method for Background Parenchymal Enhancement

Vignesh A Arasu¹, Roy Harnish¹, Cody McHargue¹, Wen Li¹, Lisa J Wilmes¹, David Newitt¹, Ella Jones¹, Laura J Esserman², Bonnie N Joe¹, and Nola M Hylton¹

¹Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States, ²Surgery, University of California, San Francisco, San Francisco, CA, United States

Automated measurements of whole breast segmentation are becoming an essential process to the development of quantitative and reproducible imaging biomarkers. We have developed a method for automated whole breast tissue segmentation and assess its performance using a test dataset, and found approximately 75% of cases had satisfactory segmentation requiring none to minimal manual modification. The current method can likely provide accurate assessment of mean background parenchymal enhancement, but further refinement of breast-chest wall boundary identification is required for other measurements (e.g. breast density).

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