Book chapter · Conference paper
TopAwaRe: Topology-Aware Registration
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.
Language: | English |
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Publisher: | Springer |
Year: | 2019 |
Pages: | 364-372 |
Proceedings: | 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention |
Series: | Lecture Notes in Computer Science (including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
Journal subtitle: | 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part II |
ISBN: | 3030322440 , 3030322459 , 9783030322441 and 9783030322458 |
ISSN: | 03029743 |
Types: | Book chapter and Conference paper |
DOI: | 10.1007/978-3-030-32245-8_41 |
ORCIDs: | Feragen, Aasa , 0000-0001-7478-8708 , 0000-0001-6114-7100 , 0000-0003-2572-9730 , 0000-0002-9516-5136 and 0000-0003-1440-7488 |