title: Automated colorectal tumour segmentation in DCE-MRI using supervoxel neighbourhood contrast characteristics.
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.
authors.forename: Benjamin
authors.initials: B
authors.lastname: Irving
authors.forename: Amalia
authors.initials: A
authors.lastname: Cifor
authors.forename: Bartłomiej W
authors.initials: BW
authors.lastname: Papiez
authors.forename: Jamie
authors.initials: J
authors.lastname: Franklin
authors.forename: Ewan M
authors.initials: EM
authors.lastname: Anderson
authors.forename: Sir Michael
authors.initials: SM
authors.lastname: Brady
authors.forename: Julia A
authors.initials: JA
authors.lastname: Schnabel
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D015179:Colorectal Neoplasms / Q000473:pathology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity