Deformable registration, or nonlinear alignment of images, is a fundamental preprocessing tool in medical imaging. State-of-the-art algorithms restrict to diffeomorphisms to regularize an otherwise ill-posed problem. In particular, such models assume that a one-to-one matching exists between any pair of images. In a range of real-life-applications, however, one image may contain objects that another does not. In such cases, the one-to-one assumption is routinely accepted as unavoidable, leading to inaccurate preprocessing and, thus, inaccuracies in the subsequent analysis. We present a novel, piecewise-diffeomorphic deformation framework which models topological changes as explicitly encoded discontinuities in the deformation fields. We thus preserve the regularization properties of diffeomorphic models while locally avoiding their erroneous one-to-one assumption. The entire model is GPU-implemented, and validated on intersubject 3D registration of T1-weighted brain MRI. Qualitative and quantitative results show our ability to improve performance in pathological cases containing topological inconsistencies.
CITATION STYLE
Nielsen, R. K., Darkner, S., & Feragen, A. (2019). TopAwaRe: Topology-Aware Registration. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11765 LNCS, pp. 364–372). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-32245-8_41
Mendeley helps you to discover research relevant for your work.