Automated colorectal tumour segmentation in DCE-MRI using supervoxel neighbourhood contrast characteristics
Irving B, Cifor A, Papiez BW, Franklin J, Anderson EM, Brady SM, Schnabel JA (2014)
Publication Type: Conference contribution
Publication year: 2014
Journal
Publisher: Springer Verlag
Book Volume: 8673 LNCS
Pages Range: 609-616
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: USA
ISBN: 9783319104034
DOI: 10.1007/978-3-319-10404-1_76
Abstract
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a powerful protocol for assessing tumour progression from changes in tissue contrast enhancement. Manual colorectal tumour delineation is a challenging and time consuming task due to the complex enhancement patterns in the 4D sequence. There is a need for a consistent approach to colorectal tumour segmentation in DCE-MRI and we propose a novel method based on detection of the tumour from signal enhancement characteristics of homogeneous tumour subregions and their neighbourhoods. Our method successfully detected 20 of 23 cases with a mean Dice score of 0.68 ± 0.15 compared to expert annotations, which is not significantly different from expert inter-rater variability of 0.73 ± 0.13 and 0.77 ± 0.10. In comparison, a standard DCE-MRI tumour segmentation technique, fuzzy c-means, obtained a Dice score of 0.28 ± 0.17. © 2014 Springer International Publishing.
Involved external institutions
University of Oxford
United Kingdom (GB)
Oxford University Hospitals NHS Foundation Trust
United Kingdom (GB)
United Kingdom (GB)
Oxford University Hospitals NHS Foundation Trust
United Kingdom (GB)
How to cite
APA:
Irving, B., Cifor, A., Papiez, B.W., Franklin, J., Anderson, E.M., Brady, S.M., & Schnabel, J.A. (2014). Automated colorectal tumour segmentation in DCE-MRI using supervoxel neighbourhood contrast characteristics. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 609-616). USA: Springer Verlag.
MLA:
Irving, Benjamin, et al. "Automated colorectal tumour segmentation in DCE-MRI using supervoxel neighbourhood contrast characteristics." Proceedings of the 17th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014, USA Springer Verlag, 2014. 609-616.
BibTeX: Download