Liver Motion Estimation via Locally Adaptive Over-Segmentation Regularization

BartŁomiej W. Papiez, Jamie Franklin, Mattias Heinrich, Fergus V. Gleeson, Julia A. Schnabel


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-enhanced Magnetic 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.
Original languageEnglish
Title of host publicationMedical Image Computing and Computer-Assisted Intervention – MICCAI 2015
EditorsNassir Navab, Joachim Hornegger, William M. Wells, Alejandro F. Frangi
Number of pages8
PublisherSpringer Vieweg, Berlin Heidelberg
Publication date18.11.2015
ISBN (Print)978-3-319-24573-7
ISBN (Electronic)978-3-319-24574-4
Publication statusPublished - 18.11.2015
Event18th International Conference on Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015
- Munich, Germany
Duration: 05.10.201509.10.2015


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