Modeling Topological Changes In Deformable Registration
Topological changes are common in brain MR images for ag- ing or disease studies. For deformable registration algorithms, which are formulated as a variational problem and solved by the minimization of certain energy functional, topological changes can cause false deformation in the resulting vector field, and affect algorithm convergence. In this work, we fo- cus on the effect of topological changes on diffeomorphic and inverse-consistent deformable registration algorithms, specif- ically, diffeomorphic demons and symmetric LDDMM. We first use a simple example to demonstrate the adverse effect of topological changes on these algorithms. Then, we propose an novel framework that can be imposed onto any existing dif- feomorphic and inverse-consistent deformable registration al- gorithm. Our framework renders these registration algorithms robust to topological changes, where the output will consist of two components. The first is a deformation field that presents only the brain structural change which is the expected vec- tor field if the topological change did not exist. The second component is a label map that provides a segmentation of the topological changes appeared in input images.
Reference and Preprint
X. Li and C. Wyatt, Modeling Topological Changes In Deformable Registration, InProceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro (ISBI'10). IEEE Press, Piscataway, NJ, USA, 360-363. [Poster]
Code and Example Data
The following archive contains the source code, scripts, and instructions for replicating the results in the paper:
Linux-x86 (GCC 3.4.3)
Comments and Bug Reports