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Open Access Green as soon as Postprint is submitted to ZB.
Liver motion estimation via locally adaptive over-segmentation regularization.
In: (Book cover Book cover International Conference on Medical Image Computing and Computer-Assisted Intervention). Berlin [u.a.]: Springer, 2015. 427-434 (Lect. Notes Comput. Sc. ; 9351)
Despite significant advances in the development of deformable registration methods, motion correction of deformable organs such as the liver remain a challenging task. This is due to not only low contrast in liver imaging, but also due to the particularly complex motion between scans primarily owing to patient breathing. In this paper, we address abdominal motion estimation using a novel regularization model that is advancing the state-of-the-art in liver registration in terms of accuracy. We propose a novel regularization of the deformation field based on spatially adaptive over-segmentation, to better model the physiological motion of the abdomen. Our quantitative analysis of abdominal Computed Tomography and dynamic contrast-enhancedMagnetic Resonance Imaging scans show a significant improvement over the state-of-the-art Demons approaches. This work also demonstrates the feasibility of segmentation-free registration between clinical scans that can inherently preserve sliding motion at the lung and liver boundary interfaces.
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Publication type
Article: Conference contribution
ISSN (print) / ISBN
0302-9743
e-ISSN
1611-3349
Conference Title
Book cover Book cover International Conference on Medical Image Computing and Computer-Assisted Intervention
Quellenangaben
Volume: 9351,
Pages: 427-434
Publisher
Springer
Publishing Place
Berlin [u.a.]
Non-patent literature
Publications
Institute(s)
Institute for Machine Learning in Biomed Imaging (IML)