Abstract #2131

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

Jacob Johnson¹, Leah Henze Bancroft¹, Ryan Zea², Diego Hernando^1,3, Scott Reeder^1,3,4,5,6, and Roberta Strigel^1,3,7

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.

This abstract and the presentation materials are available to members only; a login is required.

Join Here