In this work, we present a fast and robust spatial alignment framework, which combines automated breast segmentation and current-prior registration techniques in a multi-level fashion. First, fully automatic breast segmentation is applied to extract the breast masks that are used to obtain an initial affine transform. Then, a non-rigid registration algorithm using normalized gradient fields as similarity measure together with curvature regularization is applied. A total of 29 subjects and 58 breast MR images were collected for performance assessment. To evaluate the global registration accuracy, the volume overlap and boundary surface distance metrics are calculated, resulting in an average Dice Similarity Coefficient (DSC) of 0.96 and root mean square distance (RMSD) of 1.64 mm. In addition, to measure local registration accuracy, for each subject a radiologist annotated 10 pairs of markers in the current and prior studies representing corresponding anatomical locations. The average distance error of marker pairs dropped from 67.37 mm to 10.86 mm after applying registration.
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Lei Wang, Jan Strehlow, Jan Rühaak, Florian Weiler, Yago Diez, Albert Gubern-Merida, Susanne Diekmann, Hendrik Laue, Horst K. Hahn, "A fast alignment method for breast MRI follow-up studies using automated breast segmentation and current-prior registration," Proc. SPIE 9413, Medical Imaging 2015: Image Processing, 941334 (20 March 2015);