期刊
MEDICAL IMAGE ANALYSIS
卷 32, 期 -, 页码 69-83出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.media.2016.03.002
关键词
Parts-based graphical models; Supervoxel; Classification; Segmentation; DCE-MRI; Rectal tumour
类别
资金
- CRUK & EPSRC Cancer Imaging Centre in Oxford
- EPSRC [EP/N026993/1, EP/M000133/1] Funding Source: UKRI
- Cancer Research UK [8971, 16466] Funding Source: researchfish
- Engineering and Physical Sciences Research Council [EP/M000133/1, EP/N026993/1] Funding Source: researchfish
Rectal tumour segmentation in dynamic contrast-enhanced MRI (DCE-MRI) is a challenging task, and an automated and consistent method would be highly desirable to improve the modelling and prediction of patient outcomes from tissue contrast enhancement characteristics - particularly in routine clinical practice. A framework is developed to automate DCE-MRI tumour segmentation, by introducing: perfusion-supervoxels to over-segment and classify DCE-MRI volumes using the dynamic contrast enhancement characteristics; and the pieces-of-parts graphical model, which adds global (anatomic) constraints that further refine the supervoxel components that comprise the tumour. The framework was evaluated on 23 DCE-MRI scans of patients with rectal adenocarcinomas, and achieved a voxelwise area-under the receiver operating characteristic curve (AUC) of 0.97 compared to expert delineations. Creating a binary tumour segmentation, 21 of the 23 cases were segmented correctly with a median Dice similarity coefficient (DSC) of 0.63, which is close to the inter-rater variability of this challenging task. A second study is also included to demonstrate the method's generalisability and achieved a DSC of 0.71. The framework achieves promising results for the underexplored area of rectal tumour segmentation in DCE-MRI, and the methods have potential to be applied to other DCE-MRI and supervoxel segmentation problems. (C) 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license.
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