Liver motion estimation via locally adaptive over-segmentation regularization

Papiez BW, Franklin J, Heinrich MP, Gleeson FV, Schnabel JA (2015)

Publication Type: Conference contribution

Publication year: 2015

Journal

Lecture Notes in Computer Science Springer Verlag

Publisher: Springer Verlag

Book Volume: 9351

Pages Range: 427-434

Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Event location: Munich, DEU

ISBN: 9783319245737

DOI: 10.1007/978-3-319-24574-4_51

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-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.

Involved external institutions

University of Oxford GB United Kingdom (GB) Universität zu Lübeck DE Germany (DE) Oxford University Hospitals NHS Foundation Trust GB United Kingdom (GB)

How to cite

APA:

Papiez, B.W., Franklin, J., Heinrich, M.P., Gleeson, F.V., & Schnabel, J.A. (2015). Liver motion estimation via locally adaptive over-segmentation regularization. In Alejandro F. Frangi, Nassir Navab, Joachim Hornegger, William M. Wells (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 427-434). Munich, DEU: Springer Verlag.

MLA:

Papiez, Bartlomiej W., et al. "Liver motion estimation via locally adaptive over-segmentation regularization." Proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015, Munich, DEU Ed. Alejandro F. Frangi, Nassir Navab, Joachim Hornegger, William M. Wells, Springer Verlag, 2015. 427-434.

BibTeX: Download