Automated colorectal tumour segmentation in DCE-MRI using supervoxel neighbourhood contrast characteristics - Irving, Benjamin; Cifor, Amalia; Papiez, Bartłomiej W.; Franklin, Jamie; Anderson, Ewan M.; Brady, Sir Michael; Schnabel, Julia A. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2014)
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.
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