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Proceedings Paper

A fast alignment method for breast MRI follow-up studies using automated breast segmentation and current-prior registration
Author(s): Lei Wang; Jan Strehlow; Jan Rühaak; Florian Weiler; Yago Diez; Albert Gubern-Merida; Susanne Diekmann; Hendrik Laue; Horst K. Hahn
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Paper Abstract

In breast cancer screening for high-risk women, follow-up magnetic resonance images (MRI) are acquired with a time interval ranging from several months up to a few years. Prior MRI studies may provide additional clinical value when examining the current one and thus have the potential to increase sensitivity and specificity of screening. To build a spatial correlation between suspicious findings in both current and prior studies, a reliable alignment method between follow-up studies is desirable. However, long time interval, different scanners and imaging protocols, and varying breast compression can result in a large deformation, which challenges the registration process.

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.

Paper Details

Date Published: 20 March 2015
PDF: 8 pages
Proc. SPIE 9413, Medical Imaging 2015: Image Processing, 941334 (20 March 2015); doi: 10.1117/12.2082700
Show Author Affiliations
Lei Wang, Fraunhofer MEVIS (Germany)
Jan Strehlow, Fraunhofer MEVIS (Germany)
Jan Rühaak, Fraunhofer MEVIS (Germany)
Florian Weiler, Fraunhofer MEVIS (Germany)
Yago Diez, Univ. de Girona (Spain)
Albert Gubern-Merida, Radboud Univ. Medical Ctr. (Netherlands)
Susanne Diekmann, Fraunhofer MEVIS (Germany)
Hendrik Laue, Fraunhofer MEVIS (Germany)
Horst K. Hahn, Fraunhofer MEVIS (Germany)


Published in SPIE Proceedings Vol. 9413:
Medical Imaging 2015: Image Processing
Sébastien Ourselin; Martin A. Styner, Editor(s)

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