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Abstract #1143

Registration-based whole breast segmentation enables highly reproducible quantitative MR-based breast density

Jia Ying1, Renee Cattell1, and Chuan Huang1,2,3
1Biomedical Engineering, Stony Brook University, Stony Brook, NY, United States, 2Radiology, Stony Brook Medicine, Stony Brook, NY, United States, 3Psychiatry, Stony Brook Medicine, Stony Brook, NY, United States

Clinical determination of mammographic breast density (MD) is subjective and can also suffer from intra- and inter-reader variability. MR-based quantitative assessment of breast density has a few unique advantages over mammography. However, accurate whole breast segmentation is crucial. Manual whole breast segmentation is burdensome and current automated methods also suffer from weaknesses. In this work, a new whole breast segmentation strategy based on image registration is proposed. The test-retest reliability of breast density derived using different segmentation methods was quantitatively assessed. The results demonstrated this new segmentation method yields more reproducible values compared to existing methods.

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