Pieces-of-parts for supervoxel segmentation with global context: Application to DCE-MRI tumour delineation.
Authors: Irving, B., Franklin, J.M., Papież, B.W., Anderson, E.M., Sharma, R.A., Gleeson, F.V., Brady, S.M. and Schnabel, J.A.
Journal: Med Image Anal
Volume: 32
Pages: 69-83
eISSN: 1361-8423
DOI: 10.1016/j.media.2016.03.002
Abstract: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.
https://eprints.bournemouth.ac.uk/34607/
Source: PubMed
Pieces-of-parts for supervoxel segmentation with global context: Application to DCE-MRI tumour delineation
Authors: Franklin, J.
Journal: Medical Image Analysis
Publisher: Elsevier
ISSN: 1361-8415
https://eprints.bournemouth.ac.uk/34607/
Source: Manual
Pieces-of-parts for supervoxel segmentation with global context: Application to DCE-MRI tumour delineation.
Authors: Irving, B., Franklin, J.M., Papież, B.W., Anderson, E.M., Sharma, R.A., Gleeson, F.V., Brady, S.M. and Schnabel, J.A.
Journal: Medical image analysis
Volume: 32
Pages: 69-83
eISSN: 1361-8423
ISSN: 1361-8415
DOI: 10.1016/j.media.2016.03.002
Abstract: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.
https://eprints.bournemouth.ac.uk/34607/
Source: Europe PubMed Central
Pieces-of-parts for supervoxel segmentation with global context: Application to DCE-MRI tumour delineation
Authors: Irving, B., Franklin, J., Papiez, B.W., Anderson, E.M., Sharma, R.A. and Gleeson, F.V.
Journal: Medical Image Analysis
Volume: 32
Issue: Aug
Pages: 69-83
ISSN: 1361-8415
Abstract: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.
https://eprints.bournemouth.ac.uk/34607/
Source: BURO EPrints