Liver Motion Estimation via Locally Adaptive Over-Segmentation Regularization

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

Abstract

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.
OriginalspracheEnglisch
TitelMedical Image Computing and Computer-Assisted Intervention – MICCAI 2015
Redakteure/-innenNassir Navab, Joachim Hornegger, William M. Wells, Alejandro F. Frangi
Seitenumfang8
Herausgeber (Verlag)Springer Vieweg, Berlin Heidelberg
Erscheinungsdatum18.11.2015
Auflage9351
Seiten427-434
ISBN (Print)978-3-319-24573-7
ISBN (elektronisch)978-3-319-24574-4
DOIs
PublikationsstatusVeröffentlicht - 18.11.2015
Veranstaltung18th International Conference on Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015
- Munich, Deutschland
Dauer: 05.10.201509.10.2015
https://www.miccai2015.org/

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