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Papiez, B.W.* ; Franklin, J.M.* ; Heinrich, M.P.* ; Gleeson, F.V.* ; Brady, M.* ; Schnabel, J.A.*

GIFTed Demons: Deformable image registration with local structure-preserving regularization using supervoxels for liver applications.

J. Med. Imaging 5:024001 (2018)
DOI PMC
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
Deformable image registration, a key component of motion correction in medical imaging, needs to be efficient and provides plausible spatial transformations that reliably approximate biological aspects of complex human organ motion. Standard approaches, such as Demons registration, mostly use Gaussian regularization for organ motion, which, though computationally efficient, rule out their application to intrinsically more complex organ motions, such as sliding interfaces. We propose regularization of motion based on supervoxels, which provides an integrated discontinuity preserving prior for motions, such as sliding. More precisely, we replace Gaussian smoothing by fast, structure-preserving, guided filtering to provide efficient, locally adaptive regularization of the estimated displacement field. We illustrate the approach by applying it to estimate sliding motions at lung and liver interfaces on challenging four-dimensional computed tomography (CT) and dynamic contrast-enhanced magnetic resonance imaging datasets. The results show that guided filter-based regularization improves the accuracy of lung and liver motion correction as compared to Gaussian smoothing. Furthermore, our framework achieves state-of-the-art results on a publicly available CT liver dataset.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Adaptive Regularization ; Deformable Image Registration ; Guided Image Filtering ; Supervoxels
ISSN (print) / ISBN 2329-4302
e-ISSN 2329-4310
Zeitschrift Journal of medical imaging
Quellenangaben Band: 5, Heft: 2, Seiten: , Artikelnummer: 024001 Supplement: ,
Verlag SPIE
Verlagsort Bellingham, Wash.
Begutachtungsstatus Peer reviewed
Institut(e) Institute for Machine Learning in Biomed Imaging (IML)