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Abstract #2131

Gradient tracing for segmentation of low resolution, low T1-weighted breast MR images

Jacob Johnson1, Leah Henze Bancroft1, Ryan Zea2, Diego Hernando1,3, Scott Reeder1,3,4,5,6, and Roberta Strigel1,3,7

1Radiology, UW- Madison, Madison, WI, United States, 2Biostatistics and Medical Informatics, UW- Madison, WI, United States, 3Medical Physics, UW- Madison, Madison, WI, United States, 4Medicine, UW- Madison, Madison, WI, United States, 5Biomedical Engineering, UW- Madison, Madison, WI, United States, 6Emergency Medicine, UW- Madison, Madison, WI, United States, 7Carbone Cancer Center, UW- Madison, Madison, WI, United States

Segmentation of breast MR images remains a challenge and a necessity for a variety of quantitative applications. We present a semi-automatic methodology for segmentation of breast tissue for the special case of low resolution, low flip angle chemical shift encoded MRI (CSE-MRI) with water-fat separation. User interaction is required to set the bounds of the segmentation, while the chest wall and skin are segmented automatically. The results differed with corrections by an experienced radiologist by 4.2% average error per case. The method exhibits comparable accuracy to published methods and high agreement between non-expert reviewers.

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