Academic Commons

Articles

Segmentation of 4D MR Renography Images Using Temporal Dynamics in a Level Set Framework

Song, Ting; Lee, Vivian S.; Rusinek, Henry; Chen, Qun; Bokacheva, Louisa; Laine, Andrew F.

A novel 4D level set framework was developed to segment dynamic MR images into the cortex, medulla and collecting system. The novelty of the method is that it combines information from spatial anatomical structures and temporal dynamics. The accuracy of the fully automatic 4D level set algorithm was found to be comparable to manual segmentation performed by experts on renal anatomy. The algorithm requires less than one minute to automatically segment a single kidney 4D patient data set with more than 40 time points.

Files

More About This Work

Academic Units
Biomedical Engineering
Published Here
August 12, 2010

Notes

2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro: Proceedings: May 14-17, 2008, Paris Marriott Rive Gauche Hotel & Conference Center, Paris, France (Piscataway, N.J.: IEEE, 2008), pp. 37-40.

Academic Commons provides global access to research and scholarship produced at Columbia University, Barnard College, Teachers College, Union Theological Seminary and Jewish Theological Seminary. Academic Commons is managed by the Columbia University Libraries.