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Rectal tumour segmentation in dynamic contrast-enhanced MRI (DCE-MRI) is achallenging task, and an automated and consistent method would be highlydesirable to improve the modelling and prediction of patient outcomes fromtissue contrast enhancement characteristics - particularly in routine clinicalpractice. A framework is developed to automate DCE-MRI tumour segmentation, byintroducing: perfusion-supervoxels to over-segment and classify DCE-MRI volumesusing the dynamic contrast enhancement characteristics; and the pieces-of-partsgraphical model, which adds global (anatomic) constraints that further refinethe supervoxel components that comprise the tumour. The framework was evaluatedon 23 DCE-MRI scans of patients with rectal adenocarcinomas, and achieved avoxelwise area-under the receiver operating characteristic curve (AUC) of 0.97compared to expert delineations. Creating a binary tumour segmentation, 21 ofthe 23 cases were segmented correctly with a median Dice similarity coefficient(DSC) of 0.63, which is close to the inter-rater variability of thischallenging task. A sec- ond study is also included to demonstrate the method'sgeneralisability and achieved a DSC of 0.71. The framework achieves promisingresults for the underexplored area of rectal tumour segmentation in DCE-MRI,and the methods have potential to be applied to other DCE-MRI and supervoxelsegmentation problems

Pieces-of-parts for supervoxel segmentation with global context: Application to DCE-MRI tumour delineation